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A hybrid feature selection method based on information theory and binary butterfly optimization algorithm

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 97卷 104079-104079页

作者： Sadeghian, Zohre Akbari, Ebrahim Nematzadeh, Hossein Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

Feature selection is the problem of finding the optimal subset of features for predicting class labels by removing irrelevant or redundant features. S-shaped binary butterfly optimization algorithm (S-bBOA) is a nature-inspired algorithm for solving the feature selection problems. The evidence shows that S-bBOA has a better performance in exploration, exploitation, convergence, and avoidance of getting stuck in local optimal compared to other optimization algorithms. However, S-bBOA does not consider redundancy and relevancy of features. This paper proposes Information Gain binary butterfly optimization algorithm (IG-bBOA), to overcome the S-bBOA constraints firstly. IG-bBOA maximizes both the classification accuracy and the mean of the mutual information between features and class labels. In addition, IG-bBOA also tries to minimize the number of selected features and is used within a three-phase proposed method called Ensemble Information Theory based binary butterfly optimization algorithm (EIT-bBOA). In the first phase, 80% of irrelevant and redundant features are removed using Minimal Redundancy-Maximal New Classification Information (MRMNCI) feature selection. In the second phase, the best feature subset is selected using IG-bBOA. Finally, a similarity based ranking method is used to select the final features subset. The experimental results are conducted using six standard datasets from UCI repository. The findings confirm the efficiency of the proposed method in improving the classification accuracy and selecting the best optimal features subset with minimum number of feature in most cases.

关键词： Feature selection Classification binary butterfly optimization algorithm Information theory algorithm

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binary butterfly optimization approaches for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 116卷 147-160页

作者： Arora, Sankalap Anand, Priyanka DAV Univ Jalandhar Punjab India Lovely Profess Univ Jalandhar Punjab India

In this paper, binary variants of the butterfly optimization algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： binary butterfly optimization algorithm butterfly optimization algorithm Feature selection Bio-inspired optimization

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