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Improving amphetamine-type stimulants drug classification using chaotic-based time-varying binary whale optimization algorithm
Published 2022“…A new chaotic time-varying binary whale optimization algorithm (CBWOATV) is introduced in this paper to optimize the feature selection process in Amphetamine-type Stimulants (ATS) and non-ATS drugs classification. …”
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Improved swarm intelligence algorithms with time-varying modified Sigmoid transfer function for Amphetamine-type stimulants drug classification
Published 2022“…Swarm-intelligence (SI) algorithms have received great attention in addressing various binary optimization problems such as feature selection. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…In this regard, this thesis proposes five FS methods for efficient EMG signals classification. The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
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EMG Feature Selection And Classification Using A Pbest-Guide Binary Particle Swarm Optimization
Published 2019“…In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. …”
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A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification
Published 2018“…To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). …”
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Improved intrusion detection algorithm based on TLBO and GA algorithms
Published 2021“…This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper, an improved method for intrusion detection for binary classification was presented and discussed in detail. …”
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Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…The solution of data reduction can be viewed as a search problem. Therefore, it can be solved by using population-based techniques such as Genetic Algorithm and Particle Swarm Optimization. …”
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Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
Published 2020“…The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). …”
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A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection
Published 2019“…Therefore, this study aims to solve the feature selection problem using binary particle swarm optimization (BPSO). …”
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Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification
Published 2020“…In addition, to examine the efficiency of the proposed method, two recent algorithms namely: Whale Optimization algorithm (WAO) and Dragonfly Algorithm (DA) are implemented for comparison. …”
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…In fact a data clustering method is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on our proposed algorithm; which is Enhanced Binary Particle swarm Optimization (EBPSO), (ii) To mine data using various data chunks (windows) and overcome a failure of single clustering. …”
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Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
Published 2020“…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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Enhancement of new smooth support vector machines for classification problems
Published 2011“…Hence, MKS-SSVM is extended for multiclass classification. Two popular multiclass classification methods One against All (OAA) and One against One (OAO)) were used to extend MKS-SSVM. …”
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Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection
Published 2023“…While it is a multi-objective problem, current methods tend to treat feature selection as a single-objective optimization task. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…In order to estimate the quality of ‘pruned’ features, self-adaptive DE algorithm is proposed. Two parameters (population size and generation numbers) are adaptively adopted from number of remaining ranking features. …”
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Information Theoretic-based Feature Selection for Machine Learning
Published 2018“…In these extended IFS method, feature selection method was defined and presented as a 0-1 Knapsack Problem (MKP). …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
Published 2018“…These problems were extensively studied within the scope of classification (binary and multi-class) and regression (linear and survival). …”
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