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Classification is a widely used supervised learning technique that enables models to discover the relationship between a set of features and a specified label using available data. Its applications span various fields such as engineering, telecommunication, astronomy, and medicine. In this paper, we propose a novel rule-based classifier called RUCIB (RUle-based Classifier Inspired by BRADO), which draws inspiration from the socio-inspired swarm intelligence algorithm known as BRADO. RUCIB introduces two key aspects: the ability to accommodate multiple values for features within a rule and the capability to explore all data features simultaneously. To evaluate the performance of RUCIB, we conducted experiments using ten databases sourced from the UCI machine learning database repository. In terms of classification accuracy, we compared RUCIB to ten well-known classifiers. Our results demonstrate that, on average, RUCIB outperforms Naive Bayes, SVM, PART, Hoeffding Tree, C4.5, ID3, Random Forest, CORER, CN2, and RACER by 9.32%, 8.97%, 7.58%, 7.4%, 7.34%, 7.34%, 7.22%, 5.06%, 5.01%, and 1.92%, respectively. [ABSTRACT FROM AUTHOR]

In the magnetic-suspension bearing-rotor system, the shape features of the rotor axis trajectory can directly reflect the system operation status and fault information. Accurately classifying the bearing faults can promptly find the cause of the fault and respond quickly and protect it. In this paper, in order to achieve fault identification by identifying and classifying the rotor axis trajectory, we use Hu moment invariants to extract feature vectors from the rotor axis trajectory to identify faults. We use imperial competitive algorithm to reduce redundant data and establish a directed acyclic graph support vector machine to classify and identify feature data. Compared with traditional classification models such as C4.5 and BP neural networks, the experimental results show that the support vector machine model based on Hu moment invariants and simulated annealing has a higher accuracy and robustness in small sample classification. It is suitable for intelligent identification and classification for bearing faults. [ABSTRACT FROM AUTHOR]

Machine learning techniques aim to mimic the human ability to automatically learn how to perform tasks through training examples. They have proven capable of tasks such as prediction, learning and adaptation based on experience and can be used in virtually any scientific application, ranging from biomedical, robotic, to business decision applications, and others. However, the lack of domain knowledge for a particular application can make feature extraction ineffective or even unattainable. Furthermore, even in the presence of pre-processed datasets, the iterative process of optimizing Machine Learning parameters, which do not translate from one domain to another, maybe difficult for inexperienced practitioners. To address these issues, we present in this paper a Vectorized Automated ML Pre-processIng and post-pRocEssing framework, approximately named (VAMPIRE), which implements feature extraction algorithms capable of converting large time-series recordings into datasets. Also, it introduces a new concept, the Activation Engine, which is attached to the output of a Multi Layer Perceptron and extracts the optimal threshold to apply binary classification. Moreover, a tree-based algorithm is used to achieve multi-class classification using the Activation Engine. Furthermore, the internet of things gives rise to new applications such as remote sensing and communications, so consequently applying Machine Learning to improve operation accuracy, latency, and reliability is beneficial in such systems. Therefore, all classifications in this paper were performed on the edge in order to reach high accuracy with limited resources. Moreover, forecasts were applied on three unrelated biomedical datasets, and on two other pre-processed urban and activity detection datasets. Features were extracted when required, and training and testing were performed on the Raspberry Pi remotely, where high accuracy and inference speed were achieved in every experiment. Additionally, the board remained competitive in terms of power consumption when compared with a laptop which was optimized using a Graphical Processing Unit. [ABSTRACT FROM AUTHOR]

Clustering and classification-based pattern recognition techniques are widely used in various domains. While many approaches exist to perform these tasks, it remains a difficult process to perform clustering and classification simultaneously for particular datasets. In this paper, a method based on k-nearest neighbor (KNN) is presented to classify the dataset with PSO optimized k-medoids clustering. Initial clustering with k-medoids algorithm divides the dataset into smaller and disjoint clusters featuring similarity within clusters and dissimilarity with members of other clusters. Particle swarm optimization (PSO) is an evolutionary algorithm mainly used to optimize the issues in several research areas including data analytics. The fitness function of the PSO approach mentioned in this paper is based on inertia weights that identify the particles with the best positions and velocities for optimization. Additionally, PSO uses a novel elitism concept that allows massive searching capability between the swarm of particles to achieve a better convergence rate. Because of this property, it can be applied throughout different machine learning fields. Moreover, the KNN classifier outperforms the classification task in terms of classifying the optimized particles with high accuracy. The performance of the proposed technique is evaluated by experimenting with datasets taken from open sources. The simulation results revealed that the performance of the proposed method is better than the existing methods in terms of effective clustering as well as accurate classification. [ABSTRACT FROM AUTHOR]

Cloud Computing provides a promising platform for executing large scale workflow applications with enormous computational resources to offer on-demand services. Tasks in a workflow may need different type of computing resources such as storage, compute and memory type. However, inappropriate selection of these resources may lead to higher makespan and resource wastage. In this paper, we propose an effective two-phase algorithm for provisioning of cloud resources for workflow applications by using its structural features to minimize makespan and resource wastage. The proposed approach considers the nature of the tasks which may be compute intensive, memory intensive or storage intensive. We assume a realistic cloud model similar to Amazon EC2 that provides virtual machines for different types of workloads. Most importantly, the workflow model used in our approach is assumed to contain limited information about the task which is applicable for real situation. The performance of the proposed work is measured using five benchmark scientific workflows. The simulation results show that the proposed approach outperforms two existing algorithms for all these workflows. [ABSTRACT FROM AUTHOR]

Subspace embedding is a popular technique to discover a mapping space in which the samples are expected to be represented appropriately. In recent years, graph has received increasing attention in subspace embedding and most of these related graph-based algorithms directly construct the connecting graph in original space. But some redundant information probably exists in the data with high dimension, and thus, it is hard to ensure the quality of graph. In this paper, we propose a novel discriminative subspace embedding (DSE) algorithm for classification. DSE is a supervised subspace learning method. In DSE, an intra-class graph and an inter-class graph are used to characterize the relationship among samples from the same class and different classes, respectively. DSE assumes that the embeddings of samples from the same class should be similar while different embeddings should be learned for the samples belonging to different classes. Based on this assumption, the above two graphs are constructed in mapping space. In order to enhance the quality of projections, the reconstruction of original data is also taken into consideration in DSE. Finally, some datasets are adopted to test the performance of DSE. Experimental results illustrate that effective representations can be learned by DSE and it has a more competitive learning ability, in comparison with related algorithms. [ABSTRACT FROM AUTHOR]

The case study of plant intelligence inspired novel non-swarm intelligence (NSI) algorithms, namely Venus Flytrap Optimization and Bladder-Worts Suction, concentrated in this paper. These algorithms devised on the prey-hunting mechanisms of the Venus Flytrap (Dionaea Muscipula) and BladderWorts (Utricularia) plants, respectively. A comparative view of these algorithms is discussed. The main-support criterion is the major characteristic of these approaches. The benefits of this main-support criterion and their performances are evidenced with a case study of extracting the highly correlated maximal local patterns in gene expression data through biclustering. The NSI algorithms are proposed for biclustering gene expression data in this paper. The results are compared with existing optimization techniques like PSO and GA, and biclustering approaches like Cheng and Church, OPSM, BiMax, and Plaid approaches. This analysis evidenced the performance of NSI algorithms can yield optimal maximal local patterns with high correlation. Further, various real-time research applications of NSI approaches are also discussed. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, image information is explosively growing. Traditional image classification methods are difficult to deal with huge image data and cannot meet people's requirements on the accuracy and speed of image classification. In recent years, the convolutional neural network (CNN) has been developing rapidly, and it has performed extremely well. The image classification method based on CNN breaks through the bottleneck of traditional image classification methods and becomes the mainstream image classification algorithm at present. CT image classification algorithm is one of the research hot spots in the field of medical image. The purpose of this paper is to apply convolutional neural network to CT image classification, so as to speed up CT image classification and improve the accuracy of CT image classification and so as to reduce the workload of doctors and improve work efficiency. In this paper, CT images are classified by CDBN model. Vector machine SVM is used as the feature classifier of CDBN model to enhance feature transfer and reuse so as to enrich the features. It also suppresses features that are not very useful for current tasks and improves the performance of the model. Using CDBN to classify CT images, several commonly used gray images are compared. Comparing the results of the ordinary gradient algorithm with Adam algorithm, we can get the CDBN model using Adam optimization algorithm. In CT image classification, both accuracy and speed have a good effect. The experimental results show that the training speed of CDBN model of Adam optimization algorithm in CT image classification is 3% faster than that of general gradient algorithm. [ABSTRACT FROM AUTHOR]

In recent years, with the increasing volume of databases, the removal of redundant features has become an essential thing in classification. A smaller subset of features is selected using feature selection algorithm. One of the famous algorithms of feature selection methods is the crow search algorithm (CSA). This algorithm's popularity can be mentioned in the algorithm's implementation and process and the impressive results compared to the previous algorithms. Despite all these benefits, this algorithm suffers from problems such as unbalanced global and local search. It is also stuck in local optimization due to the search approach's inadequacy. In this paper, a new algorithm based on CSA is introduced. In order to overcome the shortcoming, four fundamental changes have been made to CSA. (i) The algorithm uses the concept of dynamic awareness probability to solve the balance between exploitation and exploration. Then, a new approach is introduced for each part of the search that improves crows' search performance both (ii) locally and (iii) globally. Also, as the last change, (iv) the concept of chaos is used to increase the algorithm's convergence rate. The proposed method has been tested and compared with ten well-known algorithms in this field on the same datasets and has performed on average 20% better in the feature reduction index and 2.5% in the fitness index, while has a lower performance in accuracy by only 1.5%. Practical results show that the algorithm changes have provided attractive results compared to other algorithms in this field in the mentioned metrics. [ABSTRACT FROM AUTHOR]

Ensemble technique and under-sampling technique are both effective tools used for imbalanced dataset classification problems. In this paper, a novel ensemble method combining the advantages of both ensemble learning for biasing classifiers and a new under-sampling method is proposed. The under-sampling method is named Binary PSO instance selection; it gathers with ensemble classifiers to find the most suitable length and combination of the majority class samples to build a new dataset with minority class samples. The proposed method adopts multi-objective strategy, and contribution of this method is a notable improvement of the performances of imbalanced classification, and in the meantime guaranteeing a best integrity possible for the original dataset. We experimented the proposed method and compared its performance of processing imbalanced datasets with several other conventional basic ensemble methods. Experiment is also conducted on these imbalanced datasets using an improved version where ensemble classifiers are wrapped in the Binary PSO instance selection. According to experimental results, our proposed methods outperform single ensemble methods, state-of-the-art under-sampling methods, and also combinations of these methods with the traditional PSO instance selection algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we discuss a logical approach to classification of data and identification of outlier in them. We propose an algorithm for finding all possible classes for a given subject area based on the classifier logical function. We analyze these classes and identify the most pronounced patterns corresponding to the classification principles. [ABSTRACT FROM AUTHOR]

The feature selection problem is known as one of the most critical issues with several applications in classification. Certain features of a dataset do not usually contain useful information or play important roles in the data classification. It is possible to reduce the computational burden through the elimination of unnecessary features. This paper was conducted to propose an opposition-based binary competitive optimization algorithm, namely OBCOOA, to solve the wrapper-based feature selection problems. The basic idea of the competitive optimization algorithm (COOA) lies on the natural competition between different species to survive, which is a continuous algorithm inherently. The present study entailed the main contributions as follows: primarily, a time-varying V-shape transfer function was used in the optimization process to present the binary version of the COOA algorithm, which established the right balance between the exploration and exploitation phases; secondly, in the proposed algorithm, the opposition-based learning mechanism was utilized to improve the diversity quality within the population members and to incorporate a suitable initial population. In the feature selection problem, the classification error rate and the number of selected features objective functions are usually adopted, which causes this problem to convert into a multi-objective optimization one. This research presented a single- and multi-objective approach from the proposed algorithm in order to solve the feature selection problem. The proposed algorithm was applied to 27 benchmark datasets, and the evaluation results were compared to the other well-known binary optimization algorithms. The experimental results indicated that the proposed algorithm has a better ability to find an optimal subset of features with the least classification error rate and the number of the selected features. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid sunflower-teaching learning-based optimization (SFO-TLBO) algorithm has been proposed, integrating two well-known algorithms, namely sunflower optimization, and teaching–learning-based optimization. The proposed hybrid SFO-TLBO algorithm has been incorporated based on the ratio of the fitness value of the search agent and the average fitness value of the population. The SFO has more tendency of global exploration but has a problem of slow convergence speed. On the other hand, the convergence of the TLBO algorithm is fast, but it may get stuck in local optima. In this work, we have hybridized SFO with TLBO to take advantage of both the algorithms and also to balance the trade-off between exploitation and exploration. The proposed hybrid SFO-TLBO has been first applied to nineteen test benchmark functions. The outcomes have been compared with twelve state-of-the-art algorithms. In seventeen benchmark test functions, better performance has been achieved by the proposed hybrid SFO-TLBO. Also, the proposed hybrid SFO-TLBO algorithm has been used to solve an Internet of Vehicles (IoV) optimization problem and performance has been compared with fifteen state-of-the-art algorithms. In solving the IoV problem, hybrid SFO-TLBO has been achieved better performance in comparison with all fifteen considered algorithms which showcases the applicability of hybrid SFO-TLBO algorithm in resolving large-scale, real-world problems. In addition, for feature selection problems, a binary version of hybrid SFO-TLBO has been proposed, in which a wrapper approach is used to decide an optimal subset of features with a k-nearest neighbor classifier. The proposed binary method also determines an optimal value of k for the k-nearest neighbor classifier. The binary hybrid SFO-TLBO algorithm has been applied to the QSAR biodegradation dataset for classification, and for evidencing its efficiency, its performance has been compared with the other state-of-the-art algorithms. The results of the proposed approach are quite encouraging, and an average accuracy of 89.10% has been achieved. [ABSTRACT FROM AUTHOR]

Recently, some statistical topic modeling approaches based on LDA have been applied in the field of supervised document classification, where the model generation procedure incorporates prior knowledge to improve the classification performance. However, these customizations of topic modeling are limited by the cumbersome derivation of a specific inference algorithm for each modification. In this paper, we propose a new supervised topic modeling approach for document classification problems, Neural Labeled LDA (NL-LDA), which builds on the VAE framework, and designs a special generative network to incorporate prior information. The proposed model can support semi-supervised learning based on the manifold assumption and low-density assumption. Meanwhile, NL-LDA has a consistent and concise inference method while semi-supervised learning and predicting. Quantitative experimental results demonstrate our model has outstanding performance on supervised document classification relative to the compared approaches, including traditional statistical and neural topic models. Specially, the proposed model can support both single-label and multi-label document classification. The proposed NL-LDA performs significantly well on semi-supervised classification, especially under a small amount of labeled data. Further comparisons with related works also indicate our model is competitive with state-of-the-art topic modeling approaches on semi-supervised classification. [ABSTRACT FROM AUTHOR]

Class imbalance problems have attracted the research community, but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an AdaBoost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization, and AdaBoost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of AdaBoost. [ABSTRACT FROM AUTHOR]

This paper investigates the application of Principal Component Analysis (PCA) for the development of a simple method of classification and localization of power system faults for a 150 km long transmission line. The proposed work uses only a quarter cycle pre-fault and half cycle post-fault receiving end line current signals for fast identification and isolation of the faulty line. This work analyzes current data of ten different fault classes. The fault signals are recorded at fourteen intermediate geometric locations, out of which, three locations are used for developing the PCA-ratio based classifier and a total of six locations are used for developing the localizer model. PCA is applied here to develop PC score indices, based on which, fault signature curve is developed using best fit analogy. This curve works as the key signature of localizer for each class and phase. The work is made more practically suited by incorporating noise in the signals. Thus an effort has been made in the proposed work for developing a complete practical fault diagnosis algorithm with an aim to achieve high level of accuracy both to classify and localize fault. The proposed classifier is found to produces 100% accuracy, and the localizer is found to achieve an average localization error of only 0.1189% for 40 dB SNR and 0.3965% error at a further higher noise level of 25 dB SNR, with less than 4% of maximum error. [ABSTRACT FROM AUTHOR]

An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster–Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSL-KDD. Our proposed method indicates the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure. [ABSTRACT FROM AUTHOR]

The diagnosis of heart disease is found to be a serious concern, so the diagnosis has to be done remotely and regularly to take the prior action. In the present years, the diagnosis of heart disease has become a key research area for researchers and many models have been proposed in recent years. The diagnosis of heart disease can be done using optimization algorithms, and it provides results with good efficiency. The main objective of this paper is to propose a hybrid fuzzy-based decision tree algorithm for the process of prediction of heart disease at an early stage through the continuous and remote patient monitoring system. The results obtained from the proposed algorithm are compared with the various number of classifier algorithms like decision tree J48, naïve Bayes, GA with FCM, KNN with NB, ANN, SVM with fuzzy in which the proposed HFDT algorithm provides better accuracy of 98.30%. From the above-obtained results, the proposed hybrid fuzzy-based decision tree algorithm efficiently predicts heart disease compared to the other classifier algorithms in the literature. The proposed work is implemented in the MATLAB environment using the heart disease dataset. [ABSTRACT FROM AUTHOR]

The unique properties of terahertz (THz) spectroscopy show a great potential for security and defense applications such as safe screening of persons and objects. However, a successful implementation of THz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction (DR) methods aim to reduce the dimensionality of the multivariate data and are therefore commonly used as a preprocessing step for classification algorithms and as an analytical tool allowing data visualization. In this paper, we compare the use of unsupervised and supervised DR methods for analysis and classification of THz reflection spectra based on their most widespread linear representatives, namely principal component analysis and linear discriminant analysis, respectively. To this end, both methods were applied to more than 5000 THz reflection spectra acquired from six active materials mixed at three different concentrations with polyethylene and measured at various humidity conditions. While considering scenarios with different levels of complexity, we found that the supervised approach provides better results because it enables efficient grouping despite intra-class variability. Furthermore, we showed that manipulating labels introduced into the supervised DR algorithm allows conditioning the data for a desired classification task such as security screening. Presented classification results show that simple machine learning algorithms are sufficient for highly accurate classification (>98.6%) of THz spectra, which will be suitable for many real-life applications of THz spectroscopy based on material identification. [ABSTRACT FROM AUTHOR]

The high-dimensional features in the data may affect the performance of the classification model as all of them are not useful. The selection of relevant optimal features is a tedious task, especially the data in which the number of features is high. This paper proposed a new feature selection (FS) approach based on the artificial electric field algorithm (AEFA) called FSAEFA, to select the most suitable optimal features for classification. The AEFA is an efficient and effective population-based optimization technique inspired by the principle of Coulomb's law of electrostatic force (CLEF) to solve optimization problems. The proposed FSAEFA approach can search the most suited subset of features by attracting the worst individual features towards the best individual features by using the attraction of CLEF. The proposed FSAEFA approach has been evaluated and compared with some other FS approaches on ten publicly available benchmark datasets. The experimental result has been indicated that the performance of the proposed method is superior over the existing methods in most of the cases. The significance of the proposed FS approach along with their counter parts have been statistically measured and compared by using Friedman test and Holm procedure. It has been determined that the proposed FSAEFA approach is found to be more efficient than the existing FS approaches. [ABSTRACT FROM AUTHOR]

In this paper, improved salp swarm algorithm is proposed. The algorithm integrates (1) random opposition-based learning (2) multiple leadership and (3) simulated annealing in swarm intelligence-based metaheuristic salp swarm algorithm. This integration increases the exploration and exploitation of the original salp swarm algorithm. Hence, the effectiveness of the proposed algorithm is better for complex multi-modal problems. The algorithm is tested on several standard numerical benchmark functions and CEC-2015 benchmarks. Results are compared with some well-known metaheuristics. The results represent the merit of the proposed algorithm with respect to other algorithms. The improved salp swarm algorithm is applied for feed-forward neural network training. Performance is compared with other metaheuristic-based feedforward neural network trainers for different data sets. The results show the efficiency and effectiveness of proposed algorithm in solving complex multi-modal problems. [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]

The widespread use of smartphones in recent years has led to a significant rise in the sophistication and number of Android malicious applications (apps) targeting smartphone users. Android-based smartphones attract attackers more than other smartphones due to the popularity and open source features of the Android environment. Malware apps pose a potential threat to the security of Android-based smartphones because of the storage of confidential and private information. To improve the classification efficiency of Android malicious apps, this paper proposes a hybrid approach for the classification of Android malware by integrating the fuzzy C-means clustering (FCM) algorithm with the light gradient boosting machine (LightGBM). First, the fuzzy clustering method is utilized for generating highly significant clusters of the Android apps' permissions that reflect the certain characteristics of the Android apps. The primary goal of using fuzzy clustering is to produce new features by gathering Android app permissions with related patterns together in clusters. Second, the LightGBM is employed to take the app's permissions and their clusters resulting from FCM as inputs and outputs the classification result as a malware or goodware app after the training. The advantages of using LightGBM are its high learning efficiency and precise classification. The significance of the proposed approach is illustrated by conducting several experiments using a well-known dataset containing benign and malware Android apps. The results of the experiments show that the suggested approach outperforms the other approaches and attains the highest accuracy (94.63%), area under the curve (AUC) (98.74%) and precision (97.70%). [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]

Systematization of numerical randomized functional algorithms for approximation of solutions to second-kind Fredholm integral equation is performed in this paper. Three types of algorithms are emphasized: projection algorithms, grid algorithms, and projection-grid algorithms. Disadvantages of grid algorithms that require calculation of values of the kernel of integral equations at fixed points are revealed (practically, the kernels of equation have integrable singularities and this calculation is impossible). Thus, for applied problems related to the solution of second-kind Fredholm integral equations, projection or projection-grid randomized algorithms are more convenient. [ABSTRACT FROM AUTHOR]

Bayesian uncertainty quantification of reservoir prediction is a significant area of ongoing research, with the major effort focussed on estimating the likelihood. However, the prior definition, which is equally as important in the Bayesian context and is related to the uncertainty in reservoir model description, has received less attention. This paper discusses methods for incorporating the prior definition into assisted history-matching workflows and demonstrates the impact of non-geologically plausible prior definitions on the posterior inference. This is the first of two papers to deal with the importance of an appropriate prior definition of the model parameter space, and it covers the key issue in updating the geological model—how to preserve geological realism in models that are produced by a geostatistical algorithm rather than manually by a geologist. To preserve realism, geologically consistent priors need to be included in the history-matching workflows, therefore the technical challenge lies in defining the space of all possibilities according to the current state of knowledge. This paper describes several workflows for Bayesian uncertainty quantification that build realistic prior descriptions of geological parameters for history matching using support vector regression and support vector classification. In the examples presented, it is used to build a prior description of channel dimensions, which is then used to history-match the parameters of both fluvial and deep-water reservoir geostatistical models. This paper also demonstrates how to handle modelling approaches where geological parameters and geostatistical reservoir model parameters are not the same, such as measured channel dimensions versus affinity parameter ranges of a multi-point statistics model. This can be solved using a multilayer perceptron technique to move from one parameter space to another and maintain realism. The overall workflow was implemented on three case studies, which refer to different depositional environments and geological modelling techniques, and demonstrated the ability to reduce the volume of parameter space, thereby increasing the history-matching efficiency and robustness of the quantified uncertainty. [ABSTRACT FROM AUTHOR]

In the bioinformatics field, the classification of unknown biological sequences is a key task that is fundamental for simplifying the consistency, aggregation, and survey of organisms and their evolution. We can view biological sequences as data components of higher non-fixed dimensions, corresponding to the length of the sequences. Numerical encoding performs an important function in DNA sequence evaluation via computational procedures such as one-hot encoding (OHE). However, the OHE method has drawbacks: 1) it does not add any details that may produce the additional predictive variable, and 2) if the variable has many classes, then OHE increases the feature space significantly. To overcome these drawbacks, this paper presents a computationally effective framework for classifying DNA sequences of living organisms in the image domain. The proposed strategy relies upon multilayer perceptron trained by a pseudoinverse learning autoencoder (PILAE) algorithm. The PILAE training process does not have to set the learning control parameters or indicate the number of hidden layers. Therefore, the PILAE classifier can accomplish better performance contrasting with other deep neural network (DNNs) strategies such as VGG-16 and Xception models. Experimental results have demonstrated that this proposed strategy achieves high prediction accuracy as well as to a significant degree high computational efficiency over different datasets. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is the process of finding the least possible number of features that are able to describe a dataset in the same way as the original features. Feature selection is a crucial preprocessing step for data mining techniques as it improves the performance of the prediction process in terms of speed and accuracy and also provides a better understanding of stored data. The success of the FS process depends on achieving a balance between two important factors, namely selecting the minimal number of features and maintaining the maximum accuracy in the results. In this paper, two methods are proposed to improve the FS process. Firstly, the mine blast algorithm (MBA) is introduced to optimize the FS process in the exploration phase. Secondly, the MBA is hybridized with simulated annealing as a local search in the exploitation phase to enhance the solutions located by the MBA. The proposed approaches (MBA and MBA–SA) are tested on 18 benchmark datasets from the UCI repository, and the comprehensive experimental results indicate that MBA–SA achieved good performance when compared with five approaches in the literature. [ABSTRACT FROM AUTHOR]

The problem of attaining energy efficiency in distributed systems is of importance, but a general, non-domain-specific theory of energy-minimal scheduling is far from developed. In this paper, we classify the problems of energy-minimal scheduling and present theoretical foundations of the same. We derive results concerning energy-minimal scheduling of independent jobs in a distributed system with functionally similar machines with different working and idle power ratings. The machines considered in our system can have identical as well as different speeds. If the jobs can be divided into arbitrary parts, we show that the minimum-energy schedule can be generated in linear time and give exact scheduling algorithms. For the cases where jobs are non-divisible, we prove that the scheduling problems are NP-hard and also give approximation algorithms for the same along with their bounds. [ABSTRACT FROM AUTHOR]

The qualities of new data used in the sequential learning phase of the online sequential extreme learning machine algorithm (OS-ELM) have a significant impact on the performance of OS-ELM. This paper proposes a novel data filter mechanism for OS-ELM from the perspective of fuzziness and a fuzziness-based online sequential extreme learning machine algorithm (FOS-ELM). In FOS-ELM, when new data arrive, a fuzzy classifier first picks out the meaningful data according to the fuzziness of each sample. Specifically, the new samples with high-output fuzziness are selected and then used in sequential learning. The experimental results on eight binary classification problems and three multiclass classification problems have shown that FOS-ELM updated by the new samples with high-output fuzziness has better generalization performance than OS-ELM. Since the unimportant data are discarded before sequential learning, FOS-ELM can save more memory and have higher computational efficiency. In addition, FOS-ELM can handle data one-by-one or chunk-by-chunk with fixed or varying sizes. The relationship between the fuzziness of new samples and the model performance is also studied in this paper, which is expected to provide some useful guidelines for improving the generalization ability of online sequential learning algorithms. [ABSTRACT FROM AUTHOR]

In recent years, classifier ensemble techniques have drawn the attention of many researchers in the machine learning research community. The ultimate goal of these researches is to improve the accuracy of the ensemble compared to the individual classifiers. In this paper, a novel algorithm for building ensembles called dynamic programming-based ensemble design algorithm (DPED) is introduced and studied in detail. The underlying theory behind DPED is based on cooperative game theory in the first phase and applying a dynamic programming approach in the second phase. The main objective of DPED is to reduce the size of the ensemble while encouraging extra diversity in order to improve the accuracy. The performance of the DPED algorithm is compared empirically with the classical ensemble model and with a well-known algorithm called "the most diverse." The experiments were carried out with 13 datasets from UCI and three ensemble models. Each ensemble model is constructed from 15 different base classifiers. The experimental results demonstrate that DPED outperforms the classical ensembles on all datasets in terms of both accuracy and size of the ensemble. Regarding the comparison with the most diverse algorithm, the number of selected classifiers by DPED across all datasets and all domains is less than or equal to the number selected by the most diverse algorithm. Experiment on blog spam dataset, for instance, shows that DPED provides an accuracy of 96.47 compared to 93.87 obtained by the most diverse using 40% training size. Finally, the experimental results verify the reliability, stability, and effectiveness of the proposed DPED algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an optimal weight semi-supervised learning machine for a single-hidden layer feedforward network (SLFN) with time delay is developed. Both input weights and output weights of the SLFN are globally optimized with manifold regularization. By feature mapping, input vectors can be placed at the prescribed positions in the feature space in the sense that the separability of all nonlinearly separable patterns can be maximized, unlabeled data can be leveraged to improve the classification accuracy when labeled data are scarce, and a high degree of recognition accuracy can be achieved with a small number of hidden nodes in the SLFN. Some simulation examples are presented to show the excellent performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

High dimensionality and sample imbalance of gene expression data promote the development of effective algorithms for classifying gene expression data. To improve the ability to distinguish different subtypes of gene expression data, we devise a hypervolume-based discrete evolutionary optimization algorithm (HYBDEOA) in this paper. Four objectives, namely the number of genes, the accuracy, the relevance, and the redundancy, are optimized simultaneously to guide the evolution. Firstly, binary encoding is used to choose some features, projecting data onto different subspaces. After that, a discrete neighborhood operation is conducted to generate a new binary-mapped population. Combining the new population with the current population, we employ the hypervolume-based mechanism to select the Pareto solutions. Finally, a discrete mutation method is proposed to find promising solutions in the binary search space. To demonstrate the performance of HYBDEOA, we apply HYBDEOA to 55 synthetic datasets and 35 cancer gene expression datasets. Extensive experiments are also conducted to reveal the effectiveness and efficiency of HYBDEOA. The experimental results demonstrate that our proposed method is a parameter-less and robust algorithm, which can group gene expression data with a finer and more informative classification. [ABSTRACT FROM AUTHOR]

The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized spatial information. To this end, in this paper, novel gradient-based marker selection technique, dissimilarity measures, and adaptive connection weighting method are proposed by making full use of spatial information in hyperspectral image. Concretely, for a given hyperspectral image, a pixel-wise classification is firstly performed, and meanwhile the gradient map is generated by a morphology-based algorithm. Secondly, the most reliable pixels are selected as the markers from the classification map, and then the boundary noise pixels are excluded from the marker map by using the gradient map. Thirdly, several new dissimilarity measures are proposed by incorporating gradient information or probability information of pixels. Furthermore, in the growth procedure of MSF, the connection weighting between pixels is adjusted adaptively to improve the robustness of the MSF algorithm. Finally, when building the final classification map by using the majority voting rule, the labels of the training samples are used to dominate the label prediction. Experimental results are performed on two hyperspectral image sets Indian Pines and University of Pavia with different resolutions and contexts. The proposed approach yields higher classification accuracies compared to previously proposed classification methods, and provides accurate segmentation maps. [ABSTRACT FROM AUTHOR]

In hyperspectral image (HSI) classification, a big challenge is the limited sample size with a relatively high feature dimension. Therefore, effective feature extraction of data is essential, which is desired to remove the redundancy as well as improve the discrimination. A huge number of methods have been proposed for HSI feature extraction. In recent years, deep learning-based feature extraction algorithms have shown their superiorities in various classification problems. Within them, deep PCA (DPCA) is a simple but efficient algorithm, which runs fast due to the absence of back-propagation. However, DPCA fails to provide satisfactory classification accuracies on HSI datasets. In this paper, we try to combine DPCA with residual-based multi-scale feature extraction and propose a residual deep PCA (RDPCA) feature extraction algorithm for HSI classification. It is a hierarchical approach consisting of multiple layers. Within each layer, PCA is utilized for layer-wise feature extraction, and the reconstruction residual is fed into the next layer. When the feature is passed deeper into the RDPCA network, finer details are mined. The layer-wise features are concatenated to form the final output feature. Furthermore, to enhance the ability of nonlinear feature extraction, we add activation functions between adjacent layers. Experimental results on real-world HSI datasets have shown the superiority of the proposed RDPCA over DPCA and PCA. [ABSTRACT FROM AUTHOR]

Despite the rapid improvement in dental health over the last few decades, a significant portion of our population continue seek dental care every year. Estimates show that 13% of adults seek dental care for dental infection or fatigue within four years. The Social and individual burden of this disease can be reduced by its early detection. However, the symptoms of teeth infection in the early stages are not clear, hence, it would be relatively difficult to predict teeth infections based solely on human skills and experience. Big Data (BD) technologies have a great potential in transforming dental care, as they have revolutionized other industries. In addition to reducing cost, they could save millions of lives and improve patient outcomes. This paper proposes a novel integrated prediction model that extracts hidden knowledge from radiographic datasets containing a large volume of dental X-ray images and utilizes this knowledge to predict dental infections. Initially, preprocessing techniques using morphological skeleton and mean approach is applied to eliminate noise and enhance the images. Next, Multi Scale Segmented Region (MSR) approach, Watershed Approach (WA), Sobel edge Detection (SD), Histogram based Segmentation (HS), Trainable Segmentation (TS), Dual Clustering (DC), and Fuzzy C-Means clustering (FCM) are examined for image segmentation and feature extraction. Among these methods, MSR was selected for feature extraction since it outperformed other methods in terms of accuracy, specificity, precision, recall and F1-score. Then, a set of neural network classifiers are trained to identify patterns in the extracted optimized features and predict dental infections. For this purpose, we have examined Bacterial Optimized Recurrent Neural Networks (BORNN), Deep Learning Neural Networks (DANN), Genetic Optimized Neural Networks (GONN) and Adaptive Neural Networks Algorithm (ADNN). BORNN have shown maximum accuracy and Roc value (98.1% and 0.92 respectively), and minimum error values (MSE = 0.189, MAE = 0.143). The output of the proposed integrated prediction model is fed into a dental robot who proceeds with the treatment process with high accuracy and minimum delay. The proposed prediction model was implemented using a big data analytics tool called Apache SAMOA and experimental results showed its correctness and effectiveness. [ABSTRACT FROM AUTHOR]

Alcohol is widely used in various fields today. Alcohol overdose and its improper use can cause many health problems. Drinking liquids with high alcohol concentration, especially methanol, causes poisoning, and the use of colognes with high alcohol concentration causes various skin conditions and respiratory diseases. Therefore, for public health it is important to determine the type and concentration of alcohol in beverages and liquid products. In this paper, K* algorithm is used to detect and classify liquids containing high concentration of alcohol by measuring the scattering parameter of the liquids in the microwave frequency band. For this purpose, scatter parameter values of aqueous solutions of ethanol, methanol, 1-propanol and 2-propanol at different concentrations have been used as training sets. Commercial liquids with known ethanol concentrations and liquids with other known alcohol concentrations have been tested to prove the accuracy of the proposed approach. As the results show the proposed approach can classify liquids containing alcohol and their alcohol concentrations successfully and with high accuracy. [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 emerging of the Internet of things (IoT), and more, the advent of the Internet of everything have revolutionized the computer networks industry. The high diversity of IoT devices, its protocols and standards, and its limited computational resources have led to the appearance of novel security challenges. Hence, the traditional security countermeasures of encryption and authentication are insufficient. Promoting the network security is a fundamental concern for practitioners for safeguarding their economical and industrial strategies. Intrusion detection systems (IDSs) are the major solutions for protecting Internet-connected frameworks at the network-level. But, more importantly, is how to convert the traditional IDSs into intelligent IDSs that resemble the intelligent IoT. This paper presents a new approach for converting the traditional IDSs into smart, evolutionary, and multi-objective IDSs for IoT networks. Moreover, this article presents a modified algorithm for IDSs that tackles the problem of feature selection. The modified algorithm stands on the integration of multi-objective particle swarm optimization with Lévy flight randomization component (MOPSO-Lévy); the modified MOPSO-Lévy has been tested on real IoT network data that is drawn from UCI repository. MOPSO-Lévy has achieved superior performance results when compared with state-of-the-art evolutionary multi-objective algorithms. [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]

This paper consists of two parts. In the first part, organizational approaches to manage the development and implementation of complex projects on the creation of innovative products and technologies for energy-saving railway transport are proposed. Some methods for estimating and classifying projects by the degree of implementability and effectiveness are considered. A resource planning model and a corresponding algorithm for a set of projects are presented. [ABSTRACT FROM AUTHOR]

Swarm-based algorithm is best suitable when it can perform smooth balance between the exploration and exploitation as well as faster convergence by successfully avoiding local optima entrapment. At recent time, salp swarm algorithm (SSA) is developed as a nature-inspired swarm-based algorithm. It can solve continuous, nonlinear and complex in nature day-to-day life optimization problems. Like many other optimization algorithms, SSA suffers with the problem of local stagnation. This paper introduces an improved version of the SSA, which improves the performance of the existing SSA by using space transformation search (STS). The proposed algorithm is termed as STS-SSA. The STS-SSA enhances the exploration and exploitation capability in the search space and successfully avoids local optima entrapment. The STS-SSA is evaluated by considering the IEEE CEC 2017 standard benchmark function set. The efficiency and robustness of the proposed STS-SSA are measured using performance metrics, convergence analysis and statistical significance. A demonstration is given as an application of the proposed algorithm for solving a real-life problem. For this purpose, the multi-layer feed-forward network is trained using the proposed STS-SSA. The experimental results demonstrate that the developed STS-SSA can be used for solving optimization problems effectively. [ABSTRACT FROM AUTHOR]

Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

The modelability index of a dataset of molecules is a measurement of the capacity of the dataset to be modeled using a QSAR algorithm. This measure allows to predict the correct classification rate of the dataset counting the nearest neighbors to the molecules of the dataset belonging to their same class. In this paper, we propose a new measure for the prediction of the modelability of datasets based on the use of the nearest neighbors based rivality index and the centroids based rivality index. These indexes take into account the noise that the nearest neighbor belonging to a different class could generate in the results of the QSAR classification algorithm. Using thirty benchmark datasets, two types of dataset representation and six different algorithms, we show the excellent behavior of the proposed indexes, obtaining correlations with values of R2 greater than 0.9 between the correct classification rate obtained in the classification processes using five folds cross-validation and the modelability index calculated using the centroid based rivality index. [ABSTRACT FROM AUTHOR]

One effective technique that has recently been considered for solving classification problems is parametric ν -support vector regression. This method obtains a concurrent learning framework for both margin determination and function approximation and leads to a convex quadratic programming problem. In this paper we introduce a new idea that converts this problem into an unconstrained convex problem. Moreover, we propose an extension of Newton's method for solving the unconstrained convex problem. We compare the accuracy and efficiency of our method with support vector machines and parametric ν -support vector regression methods. Experimental results on several UCI benchmark data sets indicate the high efficiency and accuracy of this method. [ABSTRACT FROM AUTHOR]

Supervised classification is one of the most powerful techniques to analyze data, when a-priori information is available on the membership of data samples to classes. Since the labeling process can be both expensive and time-consuming, it is interesting to investigate semi-supervised algorithms that can produce classification models taking advantage of unlabeled samples. In this paper we propose LapReGEC, a novel technique that introduces a Laplacian regularization term in a generalized eigenvalue classifier. As a result, we produce models that are both accurate and parsimonious in terms of needed labeled data. We empirically prove that the obtained classifier well compares with other techniques, using as little as 5% of labeled points to compute the models. [ABSTRACT FROM AUTHOR]

Document representation is a key problem in document analysis and processing tasks, such as document classification, clustering and information retrieval. Especially for unstructured text data, the use of a suitable document representation method would affect the performance of the subsequent algorithms for applications and research. In this paper, we propose a novel document representation method called the conditional co-occurrence degree matrix document representation method (CCODM), which is based on word co-occurrence. CCODM not only considers the co-occurrence of terms but also considers the conditional dependencies of terms in a specific context, which leads to more available and useful structural and semantic information being retained from the original documents. Extensive experimental classification results with different supervised and unsupervised feature selection methods show that the proposed method, CCODM, achieves better performance than the vector space model, latent Dirichlet allocation, the general co-occurrence matrix representation method and the document embedding method. [ABSTRACT FROM AUTHOR]

Neural network is one of the best tools for data mining tasks due to its high accuracy. However, one of the drawbacks of neural network is its black box nature. This limitation makes neural network useless for many applications which require transparency in their decision-making process. Many algorithms have been proposed to overcome this drawback by extracting transparent rules from neural network, but still researchers are in search for algorithms that can generate more accurate and simple rules. Therefore, this paper proposes a rule extraction algorithm named Eclectic Rule Extraction from Neural Network Recursively (ERENNR), with the aim to generate simple and accurate rules. ERENNR algorithm extracts symbolic classification rules from a single-layer feed-forward neural network. The novelty of this algorithm lies in its procedure of analyzing the nodes of the network. It analyzes a hidden node based on data ranges of input attributes with respect to its output and analyzes an output node using logical combination of the outputs of hidden nodes with respect to output class. And finally it generates a rule set by proceeding in a backward direction starting from the output layer. For each rule in the set, it repeats the whole process of rule extraction if the rule satisfies certain criteria. The algorithm is validated with eleven benchmark datasets. Experimental results show that the generated rules are simple and accurate. [ABSTRACT FROM AUTHOR]

K-nearest neighbors (KNN) algorithm is a common algorithm used for classification, and also a sub-routine in various complicated machine learning tasks. In this paper, we presented a quantum algorithm (QKNN) for implementing this algorithm based on the metric of Hamming distance. We put forward a quantum circuit for computing Hamming distance between testing sample and each feature vector in the training set. Taking advantage of this method, we realized a good analog for classical KNN algorithm by setting a distance threshold value t to select k − n e a r e s t neighbors. As a result, QKNN achieves O( n ) performance which is only relevant to the dimension of feature vectors and high classification accuracy, outperforms Llyod's algorithm (Lloyd et al. 2013) and Wiebe's algorithm (Wiebe et al. 2014). [ABSTRACT FROM AUTHOR]

The nearest neighbor rule is one of the most considered algorithms for supervised learning because of its simplicity and fair performance in most cases. However, this technique has a number of disadvantages, being the low computational efficiency the most prominent one. This paper presents a strategy to overcome this obstacle in multi-class classification tasks. This strategy proposes the use of Prototype Reduction algorithms that are capable of generating a new training set from the original one to try to gather the same information with fewer samples. Over this reduced set, it is estimated which classes are the closest ones to the input sample. These classes are referred to as promising classes. Eventually, classification is performed using the original training set using the nearest neighbor rule but restricted to the promising classes. Our experiments with several datasets and significance tests show that a similar classification accuracy can be obtained compared to using the original training set, with a significantly higher efficiency. [ABSTRACT FROM AUTHOR]