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The salp swarm algorithm (SSA) is a well-known optimization algorithm that is increasingly being utilized to solve many sorts of optimization problems. However, SSA may converge to sub-optimal solutions when it is applied to discrete problems such as the feature selection (FS) problem. This paper presents the enhanced opposition-based learning salp swarm algorithm (EOSSA), which is an improved SSA algorithm for solving the FS problem in intrusion detection systems (IDS). EOSSA incorporates four improvements into the original SSA algorithm. Firstly, the opposition-based learning (OBL) method is used in the initialization step of SSA to boost its population diversity. Secondly, the Elite opposition-based learning (EOBL) is used in the improvement loop of SSA to improve its exploration ability. Third, a variable neighborhood search (VNS) method is used in the improvement loop of SSA to improve its exploration mechanism to improve the local search space. Lastly, the Sigmoid binary transform function is used to convert the continuous candidate solutions produced by SSA into discrete binary solutions. EOSSA was evaluated against eighteen popular optimization algorithms (e.g., improved salp swarm algorithm based on opposition-based learning (ISSA), SSA, particle swarm algorithm (PSO), cuckoo search (CS), bat algorithm (BA), and Harris Hawk Optimization (HHO)) using eleven popular intrusion detection datasets (CICIDS2017, CSE-CIC-IDS2018, CICDDOS2019, CIRA-CIC-DoH, Intrusion detection 2018, UNSW-NB15, NSL-KDD, Phishing Legitimate, Malmem2022, IoT, and LUFlow Network) to Detect IoT Botnet Attacks. Moreover, EOSSA was compared with four machine learning algorithms (Decision Tree (DT), logistic regression (LR), Naive Bayes (NB), and K-Nearest Neighborhood (KNN)). The overall simulation results suggested that the proposed method is superior to the other algorithms in terms of the accuracy and number of selected features. The statistical analysis of the simulation results using the Friedman and Wilcoxon signed-rank test confirms the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Previous major earthquake events have revealed that soils susceptible to liquefaction are one of the factors causing significant damages to the structures. Therefore, accurate prediction of the liquefaction phenomenon is an important task in earthquake engineering. Over the past decade, several researchers have been extensively applied machine learning (ML) methods to predict soil liquefaction. This paper presents the prediction of soil liquefaction from the SPT dataset by using relatively new and robust tree-based ensemble algorithms, namely Adaptive Boosting, Gradient Boosting Machine, and eXtreme Gradient Boosting (XGBoost). The innovation points introduced in this paper are presented briefly as follows. Firstly, Stratified Random Sampling was utilized to ensure equalized sampling between each class selection. Secondly, feature selection methods such as Recursive Feature Elimination, Boruta, and Stepwise Regression were applied to develop models with a high degree of accuracy and minimal complexity by selecting the variables with significant predictive features. Thirdly, the performance of ML algorithms with feature selection methods was compared in terms of four performance metrics, Overall Accuracy, Precision, Recall, and F-measure to select the best model. Lastly, the best predictive model was determined using a statistical significance test called Wilcoxon's sign rank test. Furthermore, computational cost analyses of the tree-based ensemble algorithms were performed based on parallel and non-parallel processing. The results of the study suggest that all developed tree-based ensemble models could reliably estimate soil liquefaction. In conclusion, according to both validation and statistical results, the XGBoost with the Boruta model achieved the most stable and better prediction performance than the other models in all considered cases. [ABSTRACT FROM AUTHOR]

Computations that mimic nature are known as nature-inspired computing. Nature presents a wealthy source of thoughts and ideas for computing. The use of natural galvanized techniques has been found to provide machine solutions to complex problems. One of the challenging issues among researchers is high-dimensional data which contains a large number of unwanted, redundant, and irrelevant features. These redundant or unwanted features reduce the accuracy of machine learning models. Therefore, to solve this problem nowadays metaheuristic techniques are being used. The paper presents both surveys as well as comparison of five metaheuristic algorithms for feature selection. A wrapper-based feature selection approach using five nature-inspired techniques for feature selection has been applied. The binary version of the five swarm-based nature-inspired algorithms (NIAs), namely particle swarm optimization, whale optimization algorithm (WOA), grey wolf optimization (GWO), firefly algorithm, and bat algorithm. WOA and GWO are recent algorithms used for finding optimal feature subsets when there is no empirical information. The S-shape transfer function has been used to convert the continuous value to binary form and K-nearest neighbor is used to calculate the classification accuracy of selected feature subsets. To validate the results of the selected NIAs eleven benchmark datasets from the UCI repository are used. The strength of each NIA has been verified using a nonparametric test called the Friedman rank and Holm test. p value obtained shows that WOA is statistically significant and performs better than other models. [ABSTRACT FROM AUTHOR]

The stability of a feature selection (FS) algorithm is one of the most crucial issues when working with a machine learning model. Until now, various stability measures based on a subset of features have been proposed. However, they lack consideration for feature ranking which is equally important to judge the robustness of algorithms. This paper proposes a novel frequency-based stability measure called rank stability (RSt) that evaluates FS algorithms on both criteria, i.e., subsets of features and feature rankings. The proposed measure evaluates the variation of feature rankings generated by FS algorithms after making a small perturbation to the training set. We mathematically justify the proposed measure based on the earlier and newly defined desirable properties. Additionally, we explore various heterogeneous ensemble techniques and compare them with traditional FS algorithms on real-world datasets. We perform extensive experiments to demonstrate that the heterogeneous ensemble techniques perform better than traditional FS algorithms with respect to the proposed measure and other performance metrics. [ABSTRACT FROM AUTHOR]

Since the number of features of the dataset is much higher than the number of patterns, the higher the dimension of the data, the greater the impact on the learning algorithm. Dimension disaster has become an important problem. Feature selection can effectively reduce the dimension of the dataset and improve the performance of the algorithm. Thus, in this paper, A feature selection algorithm based on P systems (P-FS) is proposed to exploit the parallel ability of cell-like P systems and the advantage of evolutionary algorithms in search space to select features and remove redundant information in the data. The proposed P-FS algorithm is tested on five UCI datasets and an edible oil dataset from practical applications. At the same time, the P-FS algorithm and genetic algorithm feature selection (GAFS) are compared and tested on six datasets. The experimental results show that the P-FS algorithm has good performance in classification accuracy, stability, and convergence. Thus, the P-FS algorithm is feasible in feature selection. [ABSTRACT FROM AUTHOR]

Heart disease is the most life-threatening disease globally, affecting human life very critically. On-time and precise diagnosis of heart disease is vital for the prevention and treatment of heart failure. In the earlier stage, several detection techniques were developed to detect heart disease using machine learning techniques. But the heart problems have not been detected accurately with the minimum required time. This paper presented an effective heart disease prediction system that accurately diagnoses abnormality within a short duration. The feature selection process is carried with stochastic gradient boosting with a recursive feature elimination approach. For classification, the features are clustered by using the adaptive Harris hawk optimization clustering approach. Based on clustered features, the classification is processed with an enhanced deep genetic algorithm. This method improves the performance of the deep neural network by augmenting its initial weights with an enhanced genetic algorithm, which recommends better weights for the neural network. The demonstration of the proposed work is simulated in the MATLAB platform and the dataset used in UCI machine learning repository. The experimental outcome shows that the developed feature selection and classification approach attain 98.36% and 97.3% accuracy compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments. [ABSTRACT FROM AUTHOR]

Spike trains (STs) induced by external stimuli are complex and challenging to decode the embedded spatiotemporal information. Although various methods have been developed to characterize STs, few applications have been reported in tactile discrimination applications. In this paper, a neurocomputing method based on reference spike train (RST) is proposed to establish a neural computation scheme, on which existing ST metrics could be fed into various traditional models directly. Moreover, existing metrics in the field of statistics and vector measurement are introduced together to extract more discriminative features. With the binning technique and feature selection algorithm applied, the neural computation scheme is aimed at taking advantage of as maximal as possible information contained in tactile signals. Based on our designed artificial fingertip, the effect is validated by improving the recognition accuracy from 77.6% to 83.4% when it is applied to the discrimination of eight roughness surfaces. Furthermore, properties of RST, such as spike intervals and distributions, are evaluated and it is found that RSTs with uniform distribution perform the best for tactile discrimination. [ABSTRACT FROM AUTHOR]

Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms Search (SOS) is one of the most successful meta-heuristic algorithms inspired by organisms' interaction in nature called mutualism, commensalism, and parasitism. In this paper, three SOS-based binary approaches are offered to solve the feature selection problem. In the first and second approaches, several S-shaped transfer functions and several Chaotic Tent Function-based V-shaped transfer functions called BSOSST and BSOSVT are used to make the binary SOS (BSOS). In the third approach, an advanced BSOS based on changing SOS and the chaotic Tent function operators called EBCSOS is provided. The EBCSOS algorithm uses the chaotic Tent function and the Gaussian mutation to increase usefulness and exploration. Moreover, two new operators, i.e., BMPT and BCPT, are suggested to make the commensalism and mutualism stage binary based on a chaotic function to solve the feature selection problem. Finally, the proposed BSOSST and BSOSVT methods and the advanced version of EBCSOS were implemented on 25 datasets than the basic algorithm's binary meta-heuristic algorithms. Various experiments demonstrated that the proposed EBCSOS algorithm outperformed other methods in terms of several features and accuracy. To further confirm the proposed EBCSOS algorithm, the problem of detecting spam E-mails was applied, with the results of this experiment indicating that the proposed EBCSOS algorithm significantly improved the accuracy and speed of all categories in detecting spam E-mails. [ABSTRACT FROM AUTHOR]

Classification is one of the foremost machine learning tasks in this modern era. Neural Network (NN) is one of the powerful classification techniques. NN can achieve high classification accuracy on highly imbalanced and complex datasets, but lacks in explanation of its reasoning process which limits its applicability in various domains which require transparent decision along with good accuracy. There are some techniques which extract rules from NN and make it transparent; however, attribute pruning, rule pruning and class overlap algorithms are not sufficiently effective. Therefore, this paper proposes a rule extraction algorithm, called Transparent Rule Extraction using Neural Network (TRENN) to convert NN into white box with greater emphasis on attribute pruning and rule pruning. The proposed TRENN is a pedagogical approach and an extension of one of the existing algorithms named Rule Extraction from Neural Network using Classified and Misclassified data (RxNCM). The proposed TRENN extends the RxNCM with sequential floating backward search for feature and rule selection to improve the comprehensibility of the generated rules. Besides, the proposed TRENN uses probabilistic approach for the treatment of class overlapping problem in the rule updating phase instead of reclassification used in RxNCM where the overlap may persist. Experiments are conducted with eight real datasets collected from the UCI repository. Performance of the TRENN is measured with Precision, Recall, FP-Rate, F-measure, and local and global comprehensibility. It is observed from the experimental results that TRENN performs better than Re-RX, RxNCM and RxREN. [ABSTRACT FROM AUTHOR]

In this paper, a new metaheuristic algorithm called JAYA algorithm has been adapted for feature selection. Feature selection is a typical problem in machine learning and data mining domain concerned with determining the subset of high discriminative features from the irrelevant, noisy, redundant, and high-dimensional features. JAYA algorithm is initially proposed for continuous optimization. Due to the binary nature of the feature selection problem, the JAYA algorithm is adjusted using sinusoidal (i.e., S-shape) transfer function. Furthermore, the mutation operator controlled by adaptive mutation rate ( R m ) parameter is also utilized to control the diversity during the search. The proposed binary JAYA algorithm with adaptive mutation is called BJAM algorithm. The performance of BJAM algorithm is tested using 22 real-world benchmark datasets, which vary in terms of the number of features and the number of instances. Four measures are used for performance analysis: classification accuracy, number of features, fitness values, and computational times. Initially, a comparison between binary JAYA (BJA) algorithm and the proposed BJAM algorithm is conducted to show the effect of the mutation operator in the convergence behavior. After that, the results produced by the BJAM algorithm are compared against those yielded by ten state-of-the-art methods. Surprisingly, the proposed BJAM algorithm is able to excel other comparative methods in 7 out of 22 datasets in terms of classification accuracy. This can lead to the conclusion that the proposed BJAM algorithm is an efficient algorithm for the problems belonging to the feature selection domain and is pregnant with fruitful results. [ABSTRACT FROM AUTHOR]

Due to the huge amount of data being generating from different sources, the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data optimization problems comes from many factors such as the high number of features, and the existing of lost data. The feature selection process becomes an important step in many data mining and machine learning algorithms to reduce the dimensionality of the optimization problems and increase the performance of the classification or clustering algorithms. In this paper, a set of hybrid and efficient genetic algorithms are proposed to solve feature selection problem, when the handled data has a large feature size. The proposed algorithms use a new gene-weighted mechanism that can adaptively classify the features into strong relative features, weak or redundant features, and unstable features during the evolution of the algorithm. Based on this classification, the proposed algorithm gives the strong features high priority and the weak features less priority when generating new candidate solutions. In the same time, the proposed algorithm tries to more concentrate on unstable features that sometimes appear and sometimes disappear from the best solutions of the population. The performance of proposed algorithms is investigated by using different datasets and feature selection algorithms. The results show that our proposed algorithms can outperform the other feature selection algorithms and effectively enhance the classification performance over the tested datasets. [ABSTRACT FROM AUTHOR]

Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature. [ABSTRACT FROM AUTHOR]

Due to the lack of experience and prior knowledge, the selection of the most informative features has become one of the challenging problems in many applications. Recently, many metaheuristic algorithms have widely used to solve the feature selection problem for classification tasks. In this paper, the chaotic atom search optimization (CASO) that integrates the chaotic maps into atom search optimization (ASO) is applied for wrapper feature selection. Twelve different chaotic maps are used to adjust the parameter of CASO through the optimization process, which is beneficial for enhancing the convergence rate and improving the efficiency of ASO algorithm. In this study, twenty benchmark datasets acquired from the UCI machine learning repository are used to validate the performance of CASO in feature selection. Several state-of-the-art metaheuristic algorithms are adopted to examine the efficacy and effectiveness of the proposed approach. Our results indicated that the Logistic-Tent map was the most suitable chaotic map to boost the performance of CASO. The experimental result shows the capability of CASO not only in finding the optimal solution but also in significantly improving the prediction accuracy and reducing the number of features. [ABSTRACT FROM AUTHOR]

Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect. [ABSTRACT FROM AUTHOR]

The internet has provided numerous modes for secure data transmission from one end station to another, and email is one of those. The reason behind its popular usage is its cost-effectiveness and facility for fast communication. In the meantime, many undesirable emails are generated in a bulk format for a monetary benefit called spam. Despite the fact that people have the ability to promptly recognize an email as spam, performing such task may waste time. To simplify the classification task of a computer in an automated way, a machine learning method is used. Due to limited availability of datasets for email spam, constrained data and the text written in an informal way are the most feasible issues that forced the current algorithms to fail to meet the expectations during classification. This paper proposed a novel, spam mail detection method based on the document labeling concept which classifies the new ones into ham or spam. Moreover, algorithms like Naive Bayes, Decision Tree and Random Forest (RF) are used in the classification process. Three datasets are used to evaluate how the proposed algorithm works. Experimental results illustrate that RF has higher accuracy when compared with other methods. [ABSTRACT FROM AUTHOR]

In high-dimensional data, many of the features are either irrelevant to the machine learning task or are redundant. These situations lead to two problems, firstly overfitting and secondly high computational overhead. The paper proposes a feature selection method to identify the relevant subset of features for the machine-learning task using wrapper approach. The wrapper approach uses the Binary Bat algorithm to select the set of features and One-pass Generalized Classifier Neural Network (OGCNN) to evaluate the selected set of features using a novel fitness function. The proposed fitness function accounts for the entropy of sensitivity and specificity along with accuracy of classifier and fraction of selected features. The fitness function is compared using four classifiers (Radial Basis Function Neural Network, Probabilistic Neural Network, Extreme Learning Machine and OGCNN) on six publicly available datasets. One-pass classifiers are chosen as these are computationally faster. The results suggest that OGCNN along with the novel fitness function performs well in the majority of cases. [ABSTRACT FROM AUTHOR]

The traditional way of lexical interpretation is to use some words to explain other words. These semantic information cannot be used to solve the problem of natural language processing oriented to the physical situation. Based on this, this paper proposes a new method based on the feature vector Word semantic representation model and its corresponding learning algorithm Sal-F. This algorithm applies the idea of cross-scenario learning to the alignment of "word-shape features" and uses the result of feature selection under MSAV to construct a visual semantic model Lsm-G, A semantic dictionary based on graph features is constructed. Using machine-oriented evaluation method, the average selection accuracy of this algorithm is about 70%, and the accuracy of sentence selection is up to 16%. [ABSTRACT FROM AUTHOR]

The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroencephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classification algorithms. In the preconditioning of EEG, wavelet function and IIR filter are used for noise reduction. In feature selection, 15 feature algorithms in time domain, time-frequency domain, and nonlinearity are selected to obtain 30 feature parameters. Feature selection is very important for eliminating irrelevant and redundant features. Feature selection algorithms as Fisher score, Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS), and Fast Correlation-Based Filter Solution (FCBF) were used. The paper establishes a new ensemble learning algorithm based on stacking model. The basic layers are k-Nearest Neighbor (KNN), Random Forest (RF), Extremely Randomized Trees (ERT), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost) and the second layer is a Logistic regression. Comparing classification of RF, Gradient Boosting Decision Tree (GBDT), and XGBoost, the accuracies and kappa coefficients are 96.67% and 0.96 using the proposed method. It is higher than other classification algorithms.The results show that the proposed method can accurately sleep staging using single-channel EEG and has a high ability to predict sleep staging. Graphical abstract. [ABSTRACT FROM AUTHOR]

This paper investigates the channel selection problem for anti-jamming defense in an adversarial environment. In our work, we simultaneously consider malicious jamming and co-channel interference among users, and formulate this anti-jamming defense problem as a Stackelberg game with one leader and multiple followers. Specifically, the users and jammer independently and selfishly select their respective optimal strategies and obtain the optimal channels based on their own utilities. To derive the Stackelberg Equilibrium, a hierarchical learning framework is formulated, and a hierarchical learning algorithm (HLA) is proposed. In addition, the convergence performance of the proposed HLA algorithm is analyzed. Finally, we present simulation results to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the absence of effective coal-rock recognition methods during top coal caving, we propose a new multi-class feature selection approach to detecting full coal falling/30% rock mixture falling/50% rock mixture falling/full rock falling recognition during caving. The method is based on vibration and acoustic sensors fixed under the tail beam of the hydraulic as well as signal processing techniques. Distinctive vibration and acoustic signals generate during caving that depend on the state of coal-rock mixed. Via a contribution threshold $$\lambda_{0}$$ and classification accuracy P, the optimal combination of feature attributes can be automatically and sequentially decided by using the multi-class F-score (MF-Score) feature reduction approach proposed in this paper. The main idea of the MF-Score is to construct the within-class scatter matrix with a membership function based on the aggregation degree within the sample. The support vector machine classifier has been employed to test the proposed algorithm and identified the state of the coal-rock mixed. The contribution threshold is readjusted according to the calculated feature selection criterion J( k) and user requirement, and then a feedback loop is developed to keep the dynamic of the samples. Experimental results show the good generality and effectiveness of this new approach. [ABSTRACT FROM AUTHOR]

Glaucoma is an eye disease where a loss of vision occurs as a result of progressive optic nerve damage usually associates with high intraocular pressure. A subtype of glaucoma called primary angle-closure glaucoma (PACG) has been observed to be the result of one or more mechanisms such as Pupil block, Plateau iris, Peripheral iris roll, and Lens in the anterior segment of the eye. Reliable features in anterior segment images are important for determining the specific mechanisms involved in PACG. In this paper, first the discriminant features are selected by several feature selection algorithms in the context of PACG detection based on anterior segment optical coherence tomography (AS-OCT) images, and then a novel criteria is proposed to further select more reliable features. Our approach is based on selecting the top-ranked features in each algorithm and its rank combination for selection of the best features. Compared with the features selected by the individual feature selection methods, the features selected by our method achieves the best performance in terms of the accuracy of classification of the four PACG mechanisms by using AdaBoost classifier. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis. [ABSTRACT FROM AUTHOR]

Multi-instance learning was first proposed by Dietterich et al. (Artificial Intelligence 89(1–2):31–71, 1997 ) when they were investigating the problem of drug activity prediction. Here, the training set is composed of labeled bags, each of which consists of many unlabeled instances. And the goal of this learning framework is to learn some classifier from the training set for correctly labeling unseen bags. After Dietterich et al., many studies about this new learning framework have been started and many new algorithms have been proposed, for example, DD, EM-DD, Citation- kNN and so on. All of these algorithms are working on the full data set. But as in single-instance learning, different feature in training set has different effect on the training about classifier. In this paper, we will study the problem about feature selection in multi-instance learning. We will extend the data reliability measure and make it select the key feature in multi-instance scenario. [ABSTRACT FROM AUTHOR]

We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. This problem is relevant in machine learning, statistics and signal processing. It is well known that a linear regression can benefit from knowledge that the underlying regression vector is sparse. The combinatorial problem of selecting the nonzero components of this vector can be 'relaxed' by regularizing the squared error with a convex penalty function like the ℓ norm. However, in many applications, additional conditions on the structure of the regression vector and its sparsity pattern are available. Incorporating this information into the learning method may lead to a significant decrease of the estimation error. In this paper, we present a family of convex penalty functions, which encode prior knowledge on the structure of the vector formed by the absolute values of the regression coefficients. This family subsumes the ℓ norm and is flexible enough to include different models of sparsity patterns, which are of practical and theoretical importance. We establish the basic properties of these penalty functions and discuss some examples where they can be computed explicitly. Moreover, we present a convergent optimization algorithm for solving regularized least squares with these penalty functions. Numerical simulations highlight the benefit of structured sparsity and the advantage offered by our approach over the Lasso method and other related methods. [ABSTRACT FROM AUTHOR]

Detection of masses and micro calcifications are a stimulating task for radiologists in digital mammogram images. Radiologists using Computer Aided Detection (CAD) frameworks to find the breast lesion. Micro calcification may be the early sign of breast cancer. There are different kinds of methods used to detect and recognize micro calcification from mammogram images. This paper presents an ELM (Extreme Learning Machine) algorithm for micro calcification detection in digital mammogram images. The interference of mammographic image is removed at the pre-processing stages. A multi-scale features are extracted by a feature generation model. The performance did not improve by all extracted feature, therefore feature selection is performed by nature-inspired optimization algorithm. At last, the hybridized ELM classifier taken the selected optimal features to classify malignant from benign micro calcifications. The proposed work is compared with various classifiers and it shown better performance in training time, sensitivity, specificity and accuracy. The existing approaches considered here are SVM (Support Vector Machine) and NB (Naïve Bayes classifier). The proposed detection system provides 99.04% accuracy which is the better performance than the existing approaches. The optimal selection of feature vectors and the efficient classifier improves the performance of proposed system. Results illustrate the classification performance is better when compared with several other classification approaches. [ABSTRACT FROM AUTHOR]

We propose an improved model based on LVW embedded model feature extractor and ensemble learning for improving prediction accuracy of hemodialysis timing in this paper. Due to this drawback caused by feature extraction models, we adopt an enhanced LVW embedded model to search the feature subset by stochastic strategy, which can find the best feature combination that are most beneficial to learner performance. In the model application, we present an improved integrated learners for model fusion to reduce errors caused by overfitting problem of the single classifier. We run several state-of-the-art Q&A methods as contrastive experiments. The experimental results show that the ensemble learning model based on LVW has better generalization ability (97.04%) and lower standard error (± 0.04). We adopt the model to make high-precision predictions of hemodialysis timing, and the experimental results have shown that our framework significantly outperforms several strong baselines. Our model provides strong clinical decision support for physician diagnosis and has important clinical implications. [ABSTRACT FROM AUTHOR]