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Malware detection in Internet of Things (IoT) cloud platforms is a crucial security system for securing data and devices' integrity, secrecy, and availability. IoT devices are linked to cloud-based services offering storage, calculating, and analytics abilities. However, these devices are also exposed to malware attacks that could cause significant damage. Malware detection in IoT cloud platforms involves analyzing and identifying potential threats like Trojans, viruses, ransomware, and worms. It is done through several processes, including behavior-based detection, signature-based detection, and anomaly-based detection. The study proposes a Chaos Game Optimization with improved deep learning for Malware Detection (CGOIDL-MD) technique in the IoT cloud platform. The proposed CGOIDL-MD technique majorly concentrates on the automated detection and classification of malware in the IoT cloud framework. The CGOIDL-MD method applies the CGO-based feature subset selection (CGO-FSS) approach to select features. Besides, the stacked long short-term memory sequence-to-sequence autoencoder (SLSTM-SSAE) approach was exploited for malware classification and detection. Moreover, the arithmetic optimization algorithm (AOA) technique was exploited for the hyperparameter selection technique. The simulation outcomes of the CGOIDL-MD technique were tested on the malware dataset, and the outcome can be studied from different perspectives. The experimentation outcomes illustrate the betterment of the CGOIDL-MD technique under various measures. [ABSTRACT FROM AUTHOR]

Feature subset selection is considered as the most essential pre-processing step. Metaheuristic approaches may be employed to discover a solution to difficulties in feature selection, which can be viewed as an optimisation problem. The aim of the system is to provide a hybrid binary metaheuristic algorithm that combines gorilla troop optimisation and genetic algorithm to handle the feature selection issue effectively. This new method is called GTO-GA. To ensure that the optimisation technique converges fast and properly and to enhance the exploration process, the GA were used. The suggested technique is tested for stability and robustness using 16 medical datasets taken from the Kaggle and UCI repositories. To evaluate the chosen features' performance in classification issues further. The results show that the algorithm outperforms 10 top-tier optimisation methods, including PSO, ALO, the original GTO and the SCA algorithm. The results highlighted the statistical difference, superiority and importance of the suggested feature selection strategies. [ABSTRACT FROM AUTHOR]

Urban plantation dynamics in different topographical and climatic conditions in Odisha were evaluated using linear model selection and regularisation techniques. The main objective was to evaluate how and to what extent the urban plantations respond to various climatic and edaphic conditions. The relationship between vegetation growth and climatic and soil parameters was studied using four statistical learning tools, subset selection, ridge regression, lasso, and partial least squares regression, and their performance was compared to a multiple regression model. The test MSE for the subset selection, ridge regression, lasso, and partial least squares regression models was evaluated to be 16,261.54, 12245.11, 16263.79 and 14,317.21, respectively. Results proved that statistical learning methods, namely subset selection, lasso, ridge regressions and partial least squares regression, were more accurate than multiple linear regression. From the results, it can be safely concluded that temperature shows greater correlation with the growth parameters. Precipitation also plays a vital role in vegetation dynamics. Soil parameters indicate a positive correlation with that of the growth. [ABSTRACT FROM AUTHOR]

For multivariate models, one of the important properties of a variable selection criterion is consistency, whereby the probability of selecting the true subset of explanatory variables approaches 1 as the sample size goes to infinity. However, in cases of high-dimensionality, the BIC, which is expected to be consistent under large-sample asymptotic theory, may not be consistent, and the AIC, which is said to be inconsistent under the same large-sample asymptotic theory, may be consistent. In this paper, we propose a high-dimensionality-adjusted consistent AIC called the HCAIC, which is an adjusted version of the consistent AIC proposed by Bozdogan (1987) regarding the effect of high-dimensionality through an asymptotic theory whereby the dimension of the vector of response variables allows both an increasing and a fixed condition as sample size increases. Through a series of numerical experiments, we verify that the proposed HCAIC is a high-performance information criterion in the sense that it can select the true subset of explanatory variables with high probability for any dimensionality. [ABSTRACT FROM AUTHOR]

We build on a recently proposed method for stepwise explaining the solutions to Constraint Satisfaction Problems (CSPs) in a human understandable way. An explanation here is a sequence of simple inference steps where simplicity is quantified by a cost function. Explanation generation algorithms rely on extracting Minimal Unsatisfiable Subsets (MUSs) of a derived unsatisfiable formula, exploiting a one-to-one correspondence between so-called non-redundant explanations and MUSs. However, MUS extraction algorithms do not guarantee subset minimality or optimality with respect to a given cost function. Therefore, we build on these formal foundations and address the main points of improvement, namely how to generate explanations efficiently that are provably optimal (with respect to the given cost metric). To this end, we developed (1) a hitting set-based algorithm for finding the optimal constrained unsatisfiable subsets; (2) a method for reusing relevant information across multiple algorithm calls; and (3) methods for exploiting domain-specific information to speed up the generation of explanation sequences. We have experimentally validated our algorithms on a large number of CSP problems. We found that our algorithms outperform the MUS approach in terms of explanation quality and computational time (on average up to 56 % faster than a standard MUS approach). [ABSTRACT FROM AUTHOR]

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyperparameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection. [ABSTRACT FROM AUTHOR]

Big Data applications face different types of complexities in classifications. Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data. The existing scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation. When comparing to a single model, this technique offers for improved prediction. Ensemble based feature selections parallel multiple expert's judgments on a single topic. The major goal of this research is to suggest HEFSM (Heterogeneous Ensemble Feature Selection Model), a hybrid approach that combines multiple algorithms. The major goal of this research is to suggest HEFSM (Heterogeneous Ensemble Feature Selection Model), a hybrid approach that combines multiple algorithms. Further, individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work. KNN (K-Nearest Neighbor) classifier is used to classify the big dataset obtained from the ensemble learning approach. The results found of the study have been good, proving the proposed model's efficiency in classifications in terms of the performance metrics like precision, recall, F-measure and accuracy used. [ABSTRACT FROM AUTHOR]

This work proposes an efficient and promising method for linear and nonlinear dynamic systems modeling. Unlike previous methods, we combine Fuzzy C-Means (FCM) and modified Particle Swarm Optimization (PSO) algorithms. The FCM allows clustering large nonlinear data sets (good rule-base parameters initialization). The PSO algorithm is used to optimize the parameters of the fuzzy system rules (initialized by FCM). This combination allows reducing the rule-base of TSK fuzzy model considering the three goals: have a good initialization of the parameters to be optimized, handling complex systems with ensuring a high accuracy and a low complex algorithm structure. Indeed, from a given fuzzy rule-base, FCM–PSO selects a subset of important fuzzy rules based on the threshold criterion of each rule. The interaction between the particles improves iteratively the quality of each fuzzy rule. Simulation results using two well-known benchmark functions show the efficiency of the proposed approach when compared to previous works. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) is a critical biological indicator of the eutrophic state of water bodies, emphasizing the importance of its detailed characterization and continuous monitoring. This study evaluated the performance of 10 widely used Machine Learning (ML) algorithms in deriving the spatiotemporal distribution of Chl-a from uncrewed aircraft systems (UAS) imagery. Field data for Chl-a algorithm development were collected simultaneously using an Autonomous Surface Vessel (ASV), a hand-held radiometer, and a UAS in the Western Mississippi Sound (WMS). To ensure the algorithms were developed using accurate remote sensing reflectance data, the spectral response function of the UAS was applied to the radiometer measurements. An initial dataset comprising of 85 variables was compiled, including individual spectral bands, band ratios, vegetation indices, and three-band indices. Two feature selection techniques—Sequential Backward Floating Selection and Exhaustive Feature Selection—were employed to identify the optimal subset of variables. These techniques reduced the original d-dimensional feature space to a k-dimensional space by iteratively evaluating all possible feature combinations and selecting those that achieved the highest R2 scores for each ML algorithm. Model performance was further validated using a separate dataset and assessed through metrics such as Root-Mean-Square Difference (RMSD), Mean Absolute Relative Difference (MARD), and Average Percentage Difference (APD). Among the 10 ML algorithms tested, the extreme gradient boosting (XGB) algorithm demonstrated superior performance, achieving the highest R2 score of 0.848. Using a combination of two band ratios, three vegetation indices, and three three-band indices, the XGB algorithm achieved RMSD, MARD, and APD values of 0.538 μg/L, 0.407 μg/L, and 9.83 %, respectively. The XGB algorithm was subsequently applied to a time series of UAS imagery to generate Chl-a concentration maps, which revealed the spatiotemporal distribution of Chl-a across the study area. The methodology developed in this study provides a robust framework for monitoring Chl-a in the coastal waters of the WMS using UAS imagery. Furthermore, the techniques and findings provide valuable insights for advancing water quality monitoring efforts in other regions. • Developed machine learning algorithms for Chlorophyll-a estimation. • Applied feature selection techniques to refine 85 variables to an optimal subset. • Extreme gradient boosting showed best performance with R2 of 0.848. • Achieved RMSD, MARD, and APD values of 0.538 µg/L, 0.407 µg/L, and 9.83 %, respectively. • Method supports robust Chlorophyll-a monitoring in coastal waters using drone imagery. [ABSTRACT FROM AUTHOR]

In the deeply interconnected world we live in, pieces of information link domains all around us. As graph databases embrace effectively relationships among data and allow processing and querying these connections efficiently, they are rapidly becoming a popular platform for storage that supports a wide range of domains and applications. As in the relational case, it is expected that data preserves a set of integrity constraints that define the semantic structure of the world it represents. When a database does not satisfy its integrity constraints, a possible approach is to search for a 'similar' database that does satisfy the constraints, also known as a repair. In this work, we study the problem of computing subset and superset repairs for graph databases with data values using a notion of consistency based on having a set of Reg-GXPath expressions as integrity constraints. We show that for positive fragments of Reg-GXPath these problems admit a polynomial-time algorithm, while the full expressive power of the language renders them intractable. [ABSTRACT FROM AUTHOR]

This paper examines the properties of the anti-manipulation method in voting. Such a method can be used by committees and similar bodies to ensure that votes reflect genuine preferences. The anti-manipulation method is based on the Borda Count and discourages strategic voting by excluding scores that deviate excessively from the mean. The method does not eliminate strategic voting but diminishes the motivation to apply it. We compare the properties of the Borda Count and the anti-manipulation method. The properties, which are most often found in the literature, were chosen for comparison. Thus, the following properties are examined: consistency, vulnerability to the no-show paradox, vulnerability to the subset choice condition, homogeneity, monotonicity, and vulnerability to the reversal bias paradox as well as the Condorcet winner and loser paradoxes. The anti-manipulation method fails to satisfy most of these properties. A real data example, the voting of a certain jury, is used as a counterexample in most cases. [ABSTRACT FROM AUTHOR]

Feature selection technique attempts to select and remove irrelevant features while ensuring that an informative subset of features remains in the dataset. The performance of a classifier often depends on the feature subset used for the robust classification task. In the medical and healthcare application domain, classification accuracy plays a vital role. The higher level of false negatives in medical diagnosis systems may raise the risk of patients not employing the necessary treatment they need. In this article, we have proposed an unsupervised feature selection method that underlines the concepts of rough set theory for the task of classification of high-dimensional datasets. Experiments are carried out on seven public domain healthcare and life science related datasets. The obtained experimental results justify the significance of the proposed method over five other state-of-the-art feature selection methods. [ABSTRACT FROM AUTHOR]

In this paper, we solve the feature subset selection (FSS) problem with three objective functions namely, cardinality, area under receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC) using novel multi-objective evolutionary algorithms (MOEAs). MOEAs often encounter poor convergence due to the increase in non-dominated solutions and getting entrapped in the local optima. This situation worsens when dealing with large, voluminous big and high-dimensional datasets. To address these challenges, we propose parallel, fractional dominance-based MOEAs for FSS under Spark. Further, to improve the exploitation of MOEAs, we introduce a novel batch opposition-based learning (BOP) along with a cardinality constraint on the opposite solution. Accordingly, we propose two variants, namely, BOP1 and BOP2. In BOP1, a single neighbour is randomly chosen in the opposite solution space, whereas in BOP2, a group of randomly chosen neighbours in the opposite solution space. In either case, the opposite solutions are evaluated to improve the exploitation capability of the underlying MOEAs. We observe that in terms of mean optimal objective function values and across all datasets, the proposed BOP2 variant of parallel fractional dominance-based algorithms emerges as the top performer in obtaining efficient solutions. Further, we introduce a novel metric, namely the ratio of hypervolume (HV) and inverted generated distance (IGD), HV/IGD, that combines both diversity and convergence. With respect to the mean HV/IGD computed over 20 runs and Formula 1 racing, the BOP1 variants of fractional dominance-based MOEAs outperformed other algorithms. [ABSTRACT FROM AUTHOR]

Recently, a developing count of physical objects is linked to the Internet at an unprecedented rate, calcifying the knowledge of the Internet of Things (IoT). In several paradigms of IoT applications, unmanned aerial vehicles (UAVs) and satellites for IoT have concerned much attention and are experiencing quick progress. As for UAVs, because of their superiority in maneuverability and cost, it is established an increasingly extensive consumption in several IoT scenarios like disaster relief, rapid transportation, and environment monitoring. Security remains a main problem in the IoT supported UAV networks that are solved by the employ of intrusion detection system (IDS) methods. This article aims to present a Tasmanian Devil Optimization with Deep Autoencoder for Intrusion Detection System (TDODAE-IDS) technique in IoT assisted Unmanned Aerial Vehicle Networks. The presented TDODAE-IDS technique majorly concentrates on the effectual identification of the intrusions in the IoT based UAV networks. To accomplish this, the presented TDODAE-IDS system designs a new TDO algorithm for the feature subset selection process. Moreover, the DAE model classifies the existence of intrusion in the UAV network and the hyperparameter tuning of the DAE model takes place using the dragonfly algorithm (DFA). The simulation results of the TDODAE-IDS approach were tested on a benchmark IDS dataset and the results are assessed under several measures. The comprehensive comparative analysis highlighted the enhanced outcomes of the TDODAE-IDS algorithm over other recent approaches with maximum accuracy of 99.36%. Therefore, the proposed model can be employed to accomplish security in the IoT assisted UAV networks. [ABSTRACT FROM AUTHOR]

Feature selection is a key technique for data dimensionality reduction, and there are many challenges in facing the exponential expansion phenomenon of high-dimensional decision space. In order to improve the quality of feature selection, we propose a memory interaction quadratic interpolation whale optimization algorithm based on reverse information correction (RQWOA). First, in order to improve the convergence capability of the Whale Optimization Algorithm (WOA) in high-dimensional space, we propose a quadratic interpolation mechanism with memory information interaction, improve the quality of the datum points used for quadratic interpolation through the proposed memory interaction mechanism, and accelerate the convergence speed and accuracy of the algorithm with the help of the quadratic interpolation on the algorithm exploitation capability. In addition, to address the invalid probability flip in the feature selection subset when mapping to the discrete space, we propose a reverse information correction mechanism to judge and correct each dimension in the feature subset by using the reverse complementary information provided by individuals with poor fitness values in the population, so as to better remove invalid or redundant features and improve the quality of feature selection. Finally, in the 15 high-dimensional feature datasets compared with the comparison methods, the accuracy of RQWOA is improved by 7.49 % on average, and the number of features is compressed by 80.42 % on average, which indicates that the RQWOA algorithm has stronger feature identification and redundancy removal capabilities and can better solve the feature selection problem. • Proposed quadratic interpolation mechanism with memory information interaction. • A reverse correction mechanism is proposed to resolve invalid probability flips. • An improved discrete whale optimization algorithm is proposed. • The proposed algorithm is used for high-dimensional feature selection problems. • A superior performance of the proposed algorithm in feature selection is proved. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems (MOPs) aim to obtain a set of Pareto-optimal solutions, and as the number of objectives increases, the quantity of these optimal solutions grows exponentially. However, a plethora of optimal solutions can impose significant decision stress on decision-makers. Subset selection, as the extension of a model, can extract a representative set of solutions, thereby alleviating the decision-makers' choice pressure. In addition, extending a model undoubtedly incurs additional time costs. To cope with the foregoing issues, a fast subset selection method named ranking-based subset selection (RBSS) is proposed in this paper. It can efficiently select a small number of optimal solutions within an unbounded external archive and can be directly applied to any multi-objective evolutionary algorithm. This allows it to maintain good distribution and diversity with very little time investment. We employed a ranking-based approach to map the objective space to a ranking space (an integer space) defined by us and then selected the corresponding subset in the ranking space. The well-behaved mathematical properties of the ranking space and the advantages of using integer calculations accelerated the subset selection process. Experimental results indicate that compared to several state-of-the-art subset selection methods, RBSS is capable of selecting a set of representative and diverse solutions across different types of MOPs, while consuming significantly less time. Specifically, for problems where the Pareto front is a two-dimensional manifold and a one-dimensional manifold, the time consumption of RBSS is approximately only 0.028% to 27.5% and 4.6e−4% to 0.15% of that required by other algorithms, respectively. [ABSTRACT FROM AUTHOR]

Natural language processing has had a significant growth in recent years, two key factors being the increase of processing capabilities leading to very large models and the availability of larger standardized datasets. However, not every specific task has a fair amount of available data and using more data does not always lead to better results. Therefore, we also need to focus on obtaining good results even when small datasets are provided. For this reason, we propose a model for question answering, called ZeroQA, which ranked first place when submitted in two popular leaderboards for answering multiple-choice science questions: ARC Easy and ARC Challenge. Our ZeroQA model uses no transformers fine-tuned on the ARC training dataset, and relies mainly on transfer learning using a mixture of experts. We also propose methods of selecting relevant subsets from the ARC datasets for training and we use them to analyze how our model performs with less, but well chosen training datasets. [ABSTRACT FROM AUTHOR]

This article presents an electromyography (EMG) controlled hand exoskeleton for basic movements in assisted bilateral therapy, where bimanual work is required by the user. The target users are individuals with the right hand affected by an accident or cerebrovascular problems which require passive or assisted rehabilitation. Through a Matlab GUI, the system receives, processes and classifies electromyographic signals from the user acquired by a MYO armband obtaining an accuracy of 81.2% using k-Nearest Neighbors (kNN) as the classification algorithm and Random Subset Feature Selection (RSFS) as the feature selection algorithm. Subsequently, the exoskeleton reproduces the movement detected in the user's opposite hand. The exoskeleton prototype is 8 degrees of freedom (DOF), built using 3D printing and has independent movement of the fingers. The movement controller is based on fuzzy logic. For the system performance analysis, kinematic information from a motion capture system is used to compare the trajectories in different grasping tasks of a user's hand with and without the exoskeleton with a maximum error of 10.63% and a minimum of 3.46% with the desired final position, which physically represents a difference of 1.89° and 0.07° respectively. [ABSTRACT FROM AUTHOR]

Owing to the rapid development of information science, data analysis based on machine learning has become an interdisciplinary and strategic area. Marine predators algorithm (MPA) is a novel metaheuristic algorithm inspired by the foraging strategies of marine organisms. Considering the randomness of these strategies, an improved algorithm called co-evolutionary cultural mechanism-based marine predators algorithm (CECMPA) is proposed. Through this mechanism, search agents in different spaces can share knowledge and experience to improve the performance of the native algorithm. More specifically, CECMPA has a higher probability of avoiding local optimum and can search the global optimum quickly. In this paper, it is the first to use CECMPA to perform feature subset selection and optimize hyperparameters in support vector machine (SVM) simultaneously. For performance evaluation the proposed method, it is tested on twelve datasets from the university of California Irvine (UCI) repository. Moreover, the coronavirus disease 2019 (COVID-19) can be a real-world application and is spreading in many countries. CECMPA is also applied to a COVID-19 dataset. The experimental results and statistical analysis demonstrate that CECMPA is superior to other compared methods in the literature in terms of several evaluation metrics. The proposed method has strong competitive abilities and promising prospects. [ABSTRACT FROM AUTHOR]

The grasshopper optimization algorithm is one of the recently population-based optimization techniques inspired by the behaviours of grasshoppers in nature. It is an efficient optimization algorithm and since demonstrates excellent performance in solving continuous problems, but cannot resolve directly binary optimization problems. Many optimization problems have been modelled as binary problems since their decision variables varied in binary space such as feature selection in data classification. The main goal of feature selection is to find a small size subset of feature from a sizeable original set of features that optimize the classification accuracy. In this paper, a new binary variant of the grasshopper optimization algorithm is proposed and used for the feature subset selection problem. This proposed new binary grasshopper optimization algorithm is tested and compared to five well-known swarm-based algorithms used in feature selection problem. All these algorithms are implemented and experimented assessed on twenty data sets with various sizes. The results demonstrated that the proposed approach could outperform the other tested methods. [ABSTRACT FROM AUTHOR]

Biometric authentication system uses some technique that measures the physical and biological characteristics of human to identify individuals and thus provide security to a system against fraud or intrusion. Common biometric authentication processes are vulnerable and possibility for imitation. Teeth are an important biological entity that plays a major role in forensic research to identify an individual whom cannot be identified visually. There are different algorithms used in biometric authentication. This paper proposes a unique method to recognize the human teeth by using a combination of Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) to extract significant features and an improved version of Binary Particle Swarm Optimization (BPSO) for feature selection is employed to search the feature vector space in order to obtain optimal feature subset to increase the performance rate. A combination of image pre-processing techniques like background removal, gamma intensity correction and Laplacian of Gaussian (LoG) filter are used to help in correct feature extraction. Using the shift invariance property of DFT, a circular feature extraction technique and the energy compaction property of DCT, a circular sector feature extraction method is presented. Experimental results on IvisionLab/dental-image standard database are shown which exhibit promising performance of the teeth recognition system. [ABSTRACT FROM AUTHOR]

Many multimodal multi-objective evolutionary algorithms (MMEAs) are effective in solving multimodal multi-objective problems (MMOPs), which have multiple equivalent Pareto optimal sets (PSs) mapping to the same Pareto optimal front (PF). Due to the existence of the global convergence-first mechanism, these MMEAs will remove the solutions that can improve the diversity of the decision space but have poor convergence and even lead to the loss of PS when encountering MMOPs with an imbalance between convergence and diversity in the decision space (MMOP-ICD) or an MMOP with a local PS (MMOPL). We propose a new dual-population coevolutionary algorithm to address these issues. The auxiliary population helps the main population locate areas where equivalent PSs may exist, and the main population focuses on balancing convergence and diversity in the decision space. When updating the auxiliary population, a strength local convergence quality (SLCQ) is used to explore the distribution of the equivalent PSs. When updating the main population, the new niche-based truncation strategy first deletes the solutions that contribute less to convergence. Then, a distance-based subset selection method balances the diversity between the decision and objective spaces. The comparison results show the overall performance of the proposed algorithm is significantly better than other state-of-the-art algorithms. • A new dual-population weak interaction coevolutionary mechanism (DPCE) is proposed. • An SLCQ evaluation method is proposed. • We propose a niche-based truncation strategy (NBT). • We propose a new distance-based subset selection (DSS) scheme. [ABSTRACT FROM AUTHOR]

The aim of this paper is to confirm that in the case of the analysis of large data sets, the Lasso Penalty Method (LASSO) gives better results in the process of eliminating variables for the purpose of real estate value models than classical methods such as Ridge Regression. The selection of variables for an econometric model is closely related to its quality and suitability for intelligent decision support systems applied in the processes of real estate value management in the vicinity of airports. Airports face huge compensation payments as a result of the negative noise externalities they generate for neighbouring properties. Both the scale and complexity of this phenomenon require the development of intelligent systems to support airport decisions on compensation management and to monitor the environmental effects of these decisions. The selection of variables describing property characteristics is the first step in building a cost-effective system, as many of these characteristics are qualitative in nature. While systems can feed spatial information and data available in official registers, qualitative data often requires individual assessments and field visits. Hence, assessing the suitability of a given piece of information for a model is crucial at the data collection stage, especially when long time series are being constructed. For this purpose, it is proposed to use LASSO and then to model the value of developments consisting of detached houses on sets of variables selected with this method. The results obtained for LASSO are promising. They give the best set of qualitative and quantitative explanatory variables. The method has less variability than other subset selection methods tested. LASSO reduces some coefficients and zeros others, while retaining the positive attributes of subset selection and ridge regression. Furthermore, LASSO performs variable selection and coefficient estimation simultaneously. In the perspective of large set formation, this method of variable selection can also be used in Data Mining methods for estimating the value of large sets of properties. The obtained results can successfully support the process of property value management in the vicinity of airports [ABSTRACT FROM AUTHOR]

Automatic voice condition analysis systems have been developed to automatically discriminate pathological voices from healthy ones in the context of two disorders related to exudative lesions of Reinke's space: nodules and Reinke's edema. The systems are based on acoustic features, extracted from sustained vowel recordings. Reduced subsets of features have been obtained from a larger set by a feature selection algorithm based on Whale Optimization in combination with Support Vector Machine classification. Robustness of the proposed systems is assessed by adding noise of two different types (synthetic white noise and actual noise recorded in a clinical environment) to corrupt the speech signals. Two speech databases were used for this investigation: the Massachusetts Eye and Ear Infirmary (MEEI) database and a second one specifically collected in Hospital San Pedro de Alcántara (Cáceres, Spain) for the scope of this work (UEX-Voice database). The results show that the prediction performance of the detection systems appreciably decrease when moving from MEEI to a database recorded in more realistic conditions. For both pathologies, the prediction performance declines under noisy conditions, being the effect of white noise more pronounced than the effect of noise recorded in the clinical environment. [ABSTRACT FROM AUTHOR]

A signal can be represented by sparse representation with fewer coefficients. Due to this ability, sparse representation is used in research fields such as signal compression, noise elimination, and classification. In this study, sparse coefficients of the signals were obtained by using dictionary learning and sparse representation algorithms. The obtained coefficients were used in the weight update process of three different classifiers, which were created by using AdaBoost, SVM, and LDA algorithms. So, Dictionary learning based AdaBoost classifiers were obtained. The proposed Dictionary Learning (DL) based AdaBoost classifiers classified the ECG (Electrocardiography) signals. Before classification, the feature selection process was applied to ECG signals and six different feature subsets were obtained by Discrete Wavelet Transform (DWT), First Order Statistics (FOS), T-test, Bhattacharyya, Entropy, and Wilcoxon test methods. The feature subsets were used as the new dataset. The classification process was done by the proposed method and satisfying results were obtained. The best classification accuracy was obtained as 99.75% by the proposed dictionary learning based method called as DL-AdaBoost-SVM on feature subsets obtained by DWT and Wilcoxon test methods. [ABSTRACT FROM AUTHOR]

This paper proposed a new method for learning to rank documents using enumerative feature subsetting in the presence of the implicit user feedback of the various classes of users. The objective of this research was to provide an alternative method for learning ranking functions using important subsets of the LETOR (Learning to Rank) features based on the feedback of various classes of the users identified from active subsets of the features. This research, unlike other feature engineering approaches, do not force the learner to drop the inactive features permanently; instead, it allows to learn ranking function on currently active feature subsets while keeping inactive subsets of the features in the training process. The proposed model allows the search engine to dynamically utilize the implicit user feedback of the various classes of the users in learning ranking models repeatedly. The experiments performed on the LETOR MQ2008 dataset shows that the proposed model gives better NDCG (Normalized Discounted Cumulative Gain) scores for the subsets of users in the ensemble settings. Results also show that the variance of the predicted ranking can be controlled by controlling the hyper-parameters like the probability of the selection of a subset, feedback on the subsets and the weights of each subset used in training the low-level ranker. We have used cross-entropy pairwise learner RankNet and the maximum margin type svmRank as low-level rankers, but their objective functions are modified for the feature subsets. Results obtained by bagging and boosting based ensemble methods shows that the proposed method is flexible enough to model a family of the feedback weights on the individual models and can be used to provide personalized rank learning functions to the selected subsets of the users. [ABSTRACT FROM AUTHOR]

Feature selection plays a vital role to reduce the high dimension of the feature space in the text document classification problem. The dimension reduction of feature space reduces the computation cost and improves the text classification system accuracy. Hence, the identification of a proper subset of the significant features of the text corpus is needed to classify the data in less computational time with higher accuracy. In this proposed research, a novel feature selection method which combines the document frequency and the term frequency (FS-DFTF) is used to measure the significance of a term. The optimal feature subset which is selected by our proposed work is evaluated using Naive Bayes and Support Vector Machine classifier with various popular benchmark text corpus datasets. The experimental outcome confirms that the proposed method has a better classification accuracy when compared with other feature selection techniques. [ABSTRACT FROM AUTHOR]

In light of the remarkable diversity of data, arises an interesting and challenging problem of their description and concise interpretation. In a nutshell, in the proposed description pursued in this study, we consider a framework of information granules. The study develops a general scheme composed of two functional phases: (i) clustering data and features forming segments of original data and delivering a meaningful partition of data, and (ii) development of information granules. In both phases, we discuss a suite of performance indexes quantifying the quality of segments of data and the resulting information granules. Along this line, discussed are collections of information granules and their mutual relationships. A series of publicly available data sets is used in the experiments—their granular signature is quantified, and the quality of these findings is analyzed. [ABSTRACT FROM AUTHOR]

This study introduces an Adaptive K-Nearest Neighbors methodology designed for FPGA platforms, offering substantial improvements over traditional K-Nearest Neighbors implementations. By integrating a dynamic classifier selection system, our approach enhances adaptability, enabling on-the-fly adjustments of K values and subsets of training data. This flexibility results in up to a 10.66% improvement in accuracy and significantly reduces latency, rendering our system up to 3.918 times more efficient than conventional K-Nearest Neighbors techniques. The methodology's efficacy is validated through experiments across multiple datasets, demonstrating its potential in optimizing both classification accuracy and system efficiency. The adaptive approach's ability to improve response times, along with its flexibility, positions it as an ideal solution for real-time applications and highlights the advantages of the adaptive K-Nearest Neighbors methodology in overcoming the constraints of hardware-accelerated machine learning. [ABSTRACT FROM AUTHOR]

The programmer cannot write a program without any bug. A large numbers of bugs are deposited into the bug tracking system through bug reports. To find the root cause of a bug, a meaningful and huge conversation happens between the developer and reporter. The developer (triager) reads the whole bug report and then classified according to severity. The previous researchers observed that the bug report summaries provide the more resourcefully investigate information in the bug repository to the developer as part of the severity classification task. To further investigate the relationship between bug report summary and bug severity classification. A novel approach is proposed by using swarm intelligence and machine learning approaches. Firstly the n-gram technique is used to extract the semantic features score. These features are fed into the Summary Subset Selection Phase to select the optimal summary subset. The selected subset features are fed into the feature scoring phase to provide a relative score to each feature. These optimized features are used to train the proposed model. At last Naive Bayes approach is used to classify the multiclass severity classification. The results are analyzed by using 10-fold cross-validation on three benchmark datasets showed better performance in terms of Precision, Recall and F-measure. It is observed that the performance depend on the bug report contents. If the bug report has larger data for summarization than the summarization increase the classification accuracy otherwise decrease. [ABSTRACT FROM AUTHOR]

The lung sounds produced by a human convey valuable information about the health of the respiratory system, and these signals are complex in nature. In this paper, a study was conducted to find the importance of feature selection from these signals for the purpose of classification. Feature selection is performed using two different approaches: RSFS and SFS. The experiment was conducted on a dataset of 85 samples using the (SVM, KNN, and Naïve Bayes) classifiers. The computational results obtained are promising, and the proposed feature selection techniques show better performances in terms of Precision, Recall, and F-Measures. [ABSTRACT FROM AUTHOR]

Recent research on Parkinson disease (PD) detection has shown that vocal disorders are linked to symptoms in 90% of the PD patients at early stages. Thus, there is an interest in applying vocal features to the computer-assisted diagnosis and remote monitoring of patients with PD at early stages. The contribution of this research is an increase of accuracy and a reduction of the number of selected vocal features in PD detection while using the newest and largest public dataset available. Whereas the number of features in this public dataset is 754, the number of selected features for classification ranges from 8 to 20 after using Wrappers feature subset selection. Four classifiers (k nearest neighbor , multi-layer perceptron , support vector machine and random forest) are applied to vocal-based PD detection. The proposed approach shows an accuracy of 94.7%, sensitivity of 98.4%, specificity of 92.68% and precision of 97.22%. The best resulting accuracy is obtained by using a support vector machine and it is higher than the one, which was reported on the first work to use the same dataset. In addition, the corresponding computational complexity is further reduced by selecting no more than 20 features. [ABSTRACT FROM AUTHOR]

In the financial industry, identifying useful information from big data becomes a key research topic. Since the vast number of technical indicators can be captured nowadays, the indicator selection can be used to support investment decision for different financial products concurrently; however, this process is still required experience from investors. In this article, we propose a novel recommendation system which is incorporated with technical indicators. The method of fuzzy subset selection is used to feature relevant indicators which have more impact to the variation in the transaction history. The proposed method enables automatic customization of indicators for different financial products in different markets. In particular, the least absolute distance fuzzy regression with non-symmetric lower and upper bounds is proposed to avoid extreme values in dominating the model. Furthermore, to reduce computational complexity in the subset selection, the selection algorithm operates in the frequency domain for identifying and matching key patterns and peaks in transacted volumes with the technical indicators. This method performs very effective although the number of factors is much greater than the sample size. The proposed method can benefit participants in the finance markets to customize their own trading dashboard as well as set up their own trading strategies. [ABSTRACT FROM AUTHOR]

Gene microarray classification problems are considered a challenge task since the datasets contain few number of samples with high number of genes (features). The genes subset selection in microarray data play an important role for minimizing the computational load and solving classification problems. In this paper, the Correlation-based Feature Selection (CFS) algorithm is utilized in the feature selection process to reduce the dimensionality of data and finding a set of discriminatory genes. Then, the Decision Table, JRip, and OneR are employed for classification process. The proposed approach of gene selection and classification is tested on 11 microarray datasets and the performances of the filtered datasets are compared with the original datasets. The experimental results showed that CFS can effectively screen irrelevant, redundant, and noisy features. In addition, the results for all datasets proved that the proposed approach with a small number of genes can achieve high prediction accuracy and fast computational speed. Considering the average accuracy for all the analysis of microarray data, the JRip achieved the best result as compared to Decision Table, and OneR classifier. The proposed approach has a remarkable impact on the classification accuracy especially when the data is complicated with multiple classes and high number of genes. [ABSTRACT FROM AUTHOR]

• Use of N-Dimensional Spectral Solid Angle (NSSA) for band selection. • Guidelines for the selection of two key parameters for NSSA band selection. • Band selection for the improved discrimination of spectrally similar materials. • Bands capturing absorption feature position, shape, and depth. Selecting a subset of bands from hyperspectral data can improve the discrimination of ground targets because the most distinguishing spectral features are utilized. Targets with similar spectra are particularly challenging for band selection. A band selection method using the N-dimensional Solid Spectral Angle (NSSA) was recently proposed by Tian et al. (2016) to select the most dissimilar spectral regions amongst targets, but no case studies have been conducted using data from natural targets and there are currently no guidelines for the parameter selection in the NSSA band selection method. This study uses two spectral datasets of geologic relevance (clay minerals and ultramafic rocks), each with spectrally similar materials, to establish guidelines for the selection of two parameters (k and threshold) that will enable the use of the method for practical applications. K defines the band interval (relates to feature width) from which NSSA is calculated, and the threshold defines the number of bands selected from a profile of NSSA as a function of wavelength. The first guideline consists in constraining the maximum k value based on the spectral dimensionality of the widest significant spectral feature for the materials under study. The second guideline is to use a profile of the NSSA value as a function of wavelength for each permissible k value to capture the primary wavelength regions of high NSSA values. Finally, the threshold parameter for each k is estimated from a graph of the NSSA value as a function of the number of bands. The guidelines on the parameter definition allow non-expert users to select a subset of bands while capturing both narrow and broad discriminating features. Results show that bands selected from the two datasets are in good agreement with known spectral features. Of significance is that the bands encompass a range of distinguishing and often subtle spectral characteristics that include absorption feature position, and shape (asymmetry) and depth, the same that are recognized by experts, and thus can be used to assist experts in identifying key features. Moreover, the bands selected with NSSA show improved class separation as illustrated for datasets of spectrally similar materials. When tested for the discrimination of clay minerals, a competitive method named Variable Number Variable Band Selection (VNVBS) did not provide adequate information for the selection of bands. There are several valuable band selection methods reported in the literature, but few can be applied to datasets encompassing a relatively small number of spectra and to select bands that enable the discrimination of spectrally similar materials. As demonstrated in this study, the NSSA method should be of great value to studies that require feature identification from spectral libraries either resulting from the collection of field spectra or the extraction of endmembers from imagery. [ABSTRACT FROM AUTHOR]

• The relationship between formant oscillations and jaw-tongue reference position has been stated by a biomechanical model. • A method to estimate the parameters involved in this model has been proposed by kinematics derived from accelerometry. • An absolute kinematic correlate to jaw-tongue reference position has been defined (AKV). • Two Information Theory-based divergence measurements on the probability density of the AKV have been proposed (KLD and JSD) and their performance has been assessed. • The performance of AKV pdfs in differentiating articulation kinematics between PD patients and normative subjects has been tested. Speech is a vehicular tool to detect neurological degeneration using certain accepted biomarkers derived from sustained vowels, diadochokinetic exercises or running speech. Classically the Vowel Space Area (VSA) and the Formant Centralization Ratio (FCR) have been proposed to describe dysarthria in Parkinson's Disease (PD). These features are based in global estimations of the positions of the first two formants in the representation of a vowel triangle. The aim of the paper is to give a description of speech articulation dynamics as a probability density function of the kinematic features derived from the evolution of formants in the time domain. The statistical distribution of the dynamic behavior of articulation features can be used to estimate differences between speech features from subjects with Parkinson's dysarthria relative to normative subjects. Utterances of vowels [a:, i:, u:] from a subset of 16 subjects with PD (8 males and 8 females), confronted to a subset of 16 normative subjects (8 males and 8 females) have shown that the statistical distributions of dynamic articulation features can be differentiated using information theory based estimations such as Kullback-Leibler and Jensen-Shannon Divergence (JSD). These estimations allow establishing relevant statistical differences between PD and normative subjects both for males and females, improving the differentiation capability of VSA and FCR. [ABSTRACT FROM AUTHOR]

Linear diagrams have been shown to compare favourably to better known forms of set visualization, such as Venn and Euler diagrams, in supporting non-interactive assessment of set relationships. Recent studies that compared several variants of linear diagrams have demonstrated that users perform best at tasks involving identification of intersections, disjointness and subsets when using a horizontally drawn linear diagram with thin lines representing sets and employing vertical lines as guide lines. The essential visual task the user needs to perform in order to interpret this kind of diagram is vertical alignment of parallel lines and detection of overlaps. Space-filling mosaic diagrams which support this same visual task have been used in other applications, such as the visualization of schedules of activities, where they have been shown to be superior to linear Gantt charts. In this article, we present an experimental comparison of linear and mosaic diagrams for visualization of set relationships, in terms of accuracy, time-to-answer and subjective ratings of perceived task difficulty. The findings show that the two visualizations are largely similar with respect to these measures, suggesting that the choice of one or the other may be solely guided by other visual design considerations. Mosaic diagrams might be more suitable, for instance, in cases where miniature diagrams representing overviews of relations in different collections of sets are required, such as in small-multiples displays. [ABSTRACT FROM AUTHOR]

Nowadays, fifth generation (5G) network and unmanned aerial vehicle (UAV) are more and more important in the civil and military field. Only communicating correctly in 5G network and between UAVs, they can play a role in real world. Modulation classification is the premise to ensure communication in 5G network and between UAVs correctly. However, the effects of multipath fading always exists in the 5G communication environment and UAV communication channel, which leads to severe modulation classification performance and communication performance degradation. In order to resolve this problem, we proposed a novel modulation classification algorithm that can classify signals without priori information in multipath channel. The proposed algorithm makes the mean, variance, skewness and kurtosis of wavelet transform as the feature set, then uses principal component analysis (PCA) for feature subset selection, in the end neural network is used as classifier to classify signals. The simulation results show that the proposed algorithm can achieve the much better classification accuracy than the existing methods in multipath fading channels. [ABSTRACT FROM AUTHOR]

Multimodal multi-objective optimization problems (MMOPs) are multi-objective optimization problems (MOPs) involving multiple equivalent global or local Pareto optimal solution sets (PSs). For decision-makers, not only the global optimal solution sets need to be found, but also the value of local optimal solution sets cannot be ignored. However, most multimodal multi-objective evolutionary algorithms (MMOEAs) tend to select solutions with better convergence, and it is difficult to obtain the global PSs and local PSs at the same time. Therefore, we propose a fuzzy preference indicator-based two-stage evolutionary algorithm (FPITSEA) in this paper. To evaluate more comprehensively the potential of each solution in the population for locating the global and local PS during the evolutionary process, a fuzzy preference indicator is designed in FPITSEA. The fuzzy preference indicator is used to guide the evolution of the population in the first stage to find the global and local Pareto optimal regions. Subsequently, an independent evolution strategy is implemented in the second stage to distinguish different PSs as accurately as possible while also ensuring the convergence quality of the solution set. In addition, an improved distance-based subset selection method is proposed, aiming to simultaneously improve the distribution of the solution set in the decision space and objective space. Experimental results on several test sets of MMOPs show the advantages of FPITSEA over several state-of-the-art MMOEAs. [ABSTRACT FROM AUTHOR]

Multi-modal multi-objective problems (MMOPs) arise frequently in real world applications, in which multiple Pareto-optimal solution sets (PSs) correspond to the same point on the Pareto front (PF). Diversity preservation is a crucial issue in multi-modal multi-objective evolutionary algorithms (MMOEAs), which aims at evolving the population toward the PF and equivalent PSs with a uniform distribution and good spread. In spite of many diversity preservation approaches in MMOEAs, most of them guarantee the diversity in decision space with a degraded performance in objective space. To address the above issues, this article proposes a MMEA based on dual decomposition and subset selection, which employes the weight vectors and grids to decompose both the objective and decision spaces. On the basis of dual decomposition, the mating selection are adaptively performed based on estimated optimization stages of current subproblem. Moreover, a subset with good diversity in both the objective and decision spaces are selected from the promising solutions ever collected during the evolution process. Experimental results on 22 benchmark problems demonstrate our advantages to maintain a more balanced diversity in both objective and decision spaces. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

We prove the following two basis theorems for ∑12-sets of reals: (1) Every nonthin ∑12-set has a perfect A12-subset if and only if it has a nonthin A12-subset, and this is equivalent to the statement that there is a nonconstructible real. (2) Every uncountable ∑12-set has an uncountable A12-subset if and only if either every real is constructible or ωL2 is countable. We also apply the method that proves (2) to show that if there is a nonconstructible real, then there is a perfect Π12-set with no nonempty Π12-thin subset, strengthening a result of Harrington [4]. [ABSTRACT FROM AUTHOR]

Given a family C of infinite subsets of N , we study when there is a Borel function S : 2N to 2N such that for every infinite x ∈ 2N, S(x) ∈ C and S(x) ⊆ x. We show that the family of homogeneous sets (with respect to a partition of a front) as given by the Nash-Williams' theorem admits such a Borel selector. However, we also show that the analogous result for Galvin's lemma is not true by proving that there is an Fσ tall ideal on N without a Borel selector. The proof is not constructive since it is based on complexity considerations. We construct a Π12 tall ideal on N without a tall closed subset. [ABSTRACT FROM AUTHOR]

Stable clustering is a very desirable property for the dynamical knowledge base consisting of cases indexed through the domain ontology both because the cases are constantly added to the knowledge base, and the ontology structure can be revised from time to time. An approach has been developed for the stable hierarchical clustering of cases based on their semantic integration with the domain ontology. Two variants of dimension reduction are compared: principal component extraction and unsupervised feature selection. As a criterion of stability, it is proposed to use the maximum eigenvalue of the matrix of cophenetic correlations. Studies are carried out on the basis of resampling with continuous weights drawn from lognormal distribution. As a result, it was revealed that the clustering stability decreases with increasing the number of principal components. The optimal number of components can be chosen on the basis of a trade-off between the stability and the percentage of the explained variance. By unsupervised selection the subset of concepts is significantly reduced. Based on the practice of IT consulting, dendrograms of cases on the initial semantic data matrix and on the principal components were constructed and interpreted. [ABSTRACT FROM AUTHOR]

In the framework of Bishop's constructive mathematics we introduce co-convexity as a property of subsets B of ${\left\{ {0,1} \right\}^{\rm{*}}}$ , the set of finite binary sequences, and prove that co-convex bars are uniform. Moreover, we establish a canonical correspondence between detachable subsets B of ${\left\{ {0,1} \right\}^{\rm{*}}}$ and uniformly continuous functions f defined on the unit interval such that B is a bar if and only if the corresponding function f is positive-valued, B is a uniform bar if and only if f has positive infimum, and B is co-convex if and only if f satisfies a weak convexity condition. [ABSTRACT FROM AUTHOR]

Microarray data represents a valuable tool for the identification of biomarkers associated with diseases and other biological conditions. Genes, in particular, are a type of biomarker that holds great importance for the identification and understanding of various types of tumors, including brain, lung, and breast cancers. However, a significant portion of these cancer genes are not directly associated with the target disease, which can lead to challenges during analysis, such as increased computational complexity, poor generalization, and decreased classification accuracy, among others. To address this issue, a range of techniques and algorithms have been developed to optimize the selection of the most relevant subset of cancer genes. One highly effective approach to handle this challenge is the use of Swarm Intelligent (SI) algorithms, which are known for their efficiency and effectiveness as global search agents. In this paper, we present two distinct but related sections. First, we conduct a survey of current literature from 2019 to the present, on the use of SI algorithms for optimizing the selection of an optimal subset of cancer genes. Secondly, based on the analysis and findings from the first part, a presentation of an experimental study that evaluates the efficacy of four classical SI algorithms - Particle Swarm Optimization (PSO), Salp Swarm Optimization (SSA), Firefly Algorithm (FA), and Cuckoo Search (CS) – for optimizing the selection of relevant genes in three different cancer datasets. For the experimental study, we used the Chi-square, Mutual Information, and ANOVA filter methods to individually select 100, 200, and 500 relevant genes from the identified cancer datasets. We then passed these genes as input to each of the SI algorithms. The results of the study indicate that diverse filter-wrapper combinations can effectively address the challenge of selecting cancer genes across various datasets. [ABSTRACT FROM AUTHOR]

For imbalanced data, classification efficiency degrades significantly due to the missing information for the positive class, and existing sampling schemes do not consider the distributions of samples. Additionally, the global parameters of fuzzy neighborhoods are set manually. These defects affect the effectiveness of classifier. To address these problems, we offer an adaptive fuzzy multi-neighborhood feature selection methodology with intercluster distance-based hybrid sampling for class-imbalanced data. First, the number of clusters can be defined in terms of the number of samples in the negative or positive class. The initial centers of the clusters are determined according to the number of clusters, and the dissimilarity and similarity measures are calculated by using the intercluster distances between samples. Then, the cluster center, fuzzy membership matrix, and intercluster distance are studied, and then the optimization objective function is designed. The hybrid sampling scheme can be used to combine the generated positive class samples and negative class samples and obtain a class-balanced system. Second, according to the sample distribution, the standard deviation and a set of adaptive fuzzy multi-neighborhood radii are designed. A fuzzy multi-neighborhood similarity relation is defined by introducing a Gaussian kernel model to obtain a fuzzy multi-neighborhood granule, and an improved fuzzy multi-neighborhood rough set model is provided. Uncertain measures of fuzzy neighborhood systems are evaluated by the positive region and dependency. Third, by integrating fuzzy dependence with fuzzy complementary condition entropy, fuzzy multi-neighborhood complementary mutual information is provided on two viewpoints of algebra and information. Finally, a heuristic feature subset selection methodology for imbalanced classification with hybrid sampling using fuzzy c-means clustering is studied to obtain this excellent set of features. Experiments on 26 imbalanced datasets show the effectiveness of our designed algorithm. ● The optimization objective function of hybrid sampling via intercluster distance is designed to get a class-balanced system. ● A set of adaptive fuzzy multi-neighborhood radii is designed to study the fuzzy multi-neighborhood similarity relation. ● An adaptive fuzzy multi-neighborhood rough set model is provided to assess uncertain measures of fuzzy neighborhood systems. ● Fuzzy multi-neighborhood complementary mutual information is constructed from the two viewpoints of algebra and information. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is one of the leading causes of high mortality rates among women. An early disease diagnosis is crucial in breast cancer's treatment for improving the survival rate of people who have been afflicted. Human error, incorrect diagnoses, and insufficient time make manual techniques for breast cancer diagnosis ineffective and inefficient. An automated breast cancer diagnosis system assists the medical expert in diagnosing the cancer at its earliest stage. When attempting to investigate all aspects (high dimensionality) of the medical records, the automated diagnosis system faces significant challenges. It reduces the success rate of the diagnosis system. This study presents a two-stage feature subset selection approach using quasi-opposition self-adaptive coati optimization algorithm for breast cancer classification. The proposed scheme's fitness function aims to maximize the success rate and minimize the feature selection ratio. We used the three benchmark datasets (Breast Cancer Wisconsin Diagnostic Dataset (WDBC), Breast Cancer Wisconsin Prognostic Dataset (WPBC), and Breast Cancer Wisconsin Original Dataset (WBCD)) to examine the proposed scheme's performance. The comparison analysis results show that the proposed scheme outperforms the other competitor schemes. • Hybrid Coati optimization is proposed for feature selection. • A two-stage approach is employed in feature selection problem. • Differential evolution mutation strategies enhance the global search ability. • The proposed approach is successfully evaluated on three well known datasets. • The proposed method outperformed other feature selection methods. [ABSTRACT FROM AUTHOR]

For G a group definable in a saturated model of a NIP theory T, we prove that there is a smallest type-definable subgroup H of G such that the quotient G / H is stable. This generalizes the existence of G00, the smallest type-definable subgroup of G of bounded index. [ABSTRACT FROM AUTHOR]