Binary Differential Evolution Based on Individual Entropy for Feature Subset Optimization
Tao Li,Hongbin Dong,Jing Sun +2 more
TL;DR: The experimental results show that the proposed binary differential evolution based on individual entropy (BDIE) can effectively improve the classification performance and reduce the time cost without increasing the size of the feature subset.
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Abstract: The high dimensionality of data brings great challenges to the classification accuracy and complexity of the algorithm. Feature selection technology can improve the classification performance of the algorithm effectively. In this paper, a novel binary differential evolution based on individual entropy (BDIE) is proposed. First, the individual entropy method is constructed to quantify the diversity of the population, and the relationship between population diversity and convergence is analyzed. Then, the objective function based on individual entropy is designed to evaluate the feature subset. A new binary mutation strategy is proposed, and it can effectively search the global optimal solution. In order to validate the BDIE, the datasets with different sizes and the classifiers of different types are used for testing. In addition, the well-known algorithms are introduced for comparison. The experimental results show that the proposed algorithm can effectively improve the classification performance and reduce the time cost without increasing the size of the feature subset.
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Citations
A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection
Mohamed Abdel-Basset,Doaa El-Shahat,Ibrahim El-Henawy,Victor Hugo C. de Albuquerque,Seyedali Mirjalili +4 more
TL;DR: A new Grey Wolf Optimizer algorithm integrated with a Two-phase Mutation to solve the feature selection for classification problems based on the wrapper methods to reduce the number of selected features while preserving high classification accuracy.
298
A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm
TL;DR: A Modified Differential Evolution approach to Feature Selection (MDEFS) is proposed by utilizing two new mutation strategies to create a feasible balance between exploration and exploitation and maintain the classification performance in an acceptable range concerning both the number of features and accuracy.
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An improved binary sparrow search algorithm for feature selection in data classification
TL;DR: In this paper , the improved Binary Search Algorithm (iBSSA) was proposed to solve the problem of feature selection in feature selection, which is a combinatorial NP-hard problem in which computational time increases exponentially with an increase in problem complexity.
A many-objective feature selection for multi-label classification
TL;DR: A many-objective optimization based multi-label feature selection algorithm (MMFS) is presented that can balance multiple objectives, remove irrelevant and redundant features, and obtain satisfactory classification results.
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A binary individual search strategy-based bi-objective evolutionary algorithm for high-dimensional feature selection
TL;DR: Zhang et al. as discussed by the authors proposed a binary individual search strategy-based bi-objective evolutionary algorithm to obtain the optimal feature subset with higher classification accuracy and lower feature dimensions.
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Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
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Recent advances in differential evolution: a survey and experimental analysis
Ferrante Neri,Ville Tirronen +1 more
TL;DR: Numerical results show that, among the algorithms considered in this study, the most efficient additional components in a DE framework appear to be the population size reduction and the scale factor local search.
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Feature selection based on mutual information
Muhammad Aliyu Sulaiman,Jane Labadin +1 more
- 10 Dec 2015
TL;DR: Experimental results indicate that the proposed feature selection based on mutual information criterion is capable of improving the performance of the machine learning models in terms of prediction accuracy and reduction in training time.