A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion

Xiaodong Qi; Xiabi Liu; Said Boumaraf

doi:10.1109/IJCNN.2019.8852063

A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion

Xiaodong Qi, Xiabi Liu, Said Boumaraf

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

This paper proposes an effective feature selection method based on monarch butterfly optimization and Fisher criterion. Fisher criterion is applied to evaluate the feature subsets, based on which the optimal feature subsets are searched by using monarch butterfly optimization algorithm. To combine these two components, a method is developed to binarize continuous solution vectors for deciding the feature selection. We conduct experiments on widely used UCI (University of California, Irvine) classification datasets to study the design of our algorithm and compare it with other state-of-the-art counterparts. The experimental results show that the proposed method is reasonable and effective, which achieves the best result of feature selection among the compared methods and has satisfactory efficiency.

Original language	English
Title of host publication	2019 International Joint Conference on Neural Networks, IJCNN 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728119854
DOIs	https://doi.org/10.1109/IJCNN.2019.8852063
Publication status	Published - Jul 2019
Event	2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary Duration: 14 Jul 2019 → 19 Jul 2019

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2019-July

Conference

Conference	2019 International Joint Conference on Neural Networks, IJCNN 2019
Country/Territory	Hungary
City	Budapest
Period	14/07/19 → 19/07/19

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10.1109/IJCNN.2019.8852063

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Qi, X., Liu, X., & Boumaraf, S. (2019). A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 Article 8852063 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852063

@inproceedings{36cf372f8a254c8aac36870f895b3003,

title = "A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion",

abstract = "This paper proposes an effective feature selection method based on monarch butterfly optimization and Fisher criterion. Fisher criterion is applied to evaluate the feature subsets, based on which the optimal feature subsets are searched by using monarch butterfly optimization algorithm. To combine these two components, a method is developed to binarize continuous solution vectors for deciding the feature selection. We conduct experiments on widely used UCI (University of California, Irvine) classification datasets to study the design of our algorithm and compare it with other state-of-the-art counterparts. The experimental results show that the proposed method is reasonable and effective, which achieves the best result of feature selection among the compared methods and has satisfactory efficiency.",

author = "Xiaodong Qi and Xiabi Liu and Said Boumaraf",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Joint Conference on Neural Networks, IJCNN 2019 ; Conference date: 14-07-2019 Through 19-07-2019",

year = "2019",

month = jul,

doi = "10.1109/IJCNN.2019.8852063",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

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Qi, X, Liu, X & Boumaraf, S 2019, A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion. in 2019 International Joint Conference on Neural Networks, IJCNN 2019., 8852063, Proceedings of the International Joint Conference on Neural Networks, vol. 2019-July, Institute of Electrical and Electronics Engineers Inc., 2019 International Joint Conference on Neural Networks, IJCNN 2019, Budapest, Hungary, 14/07/19. https://doi.org/10.1109/IJCNN.2019.8852063

A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion. / Qi, Xiaodong; Liu, Xiabi; Boumaraf, Said.
2019 International Joint Conference on Neural Networks, IJCNN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8852063 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion

AU - Qi, Xiaodong

AU - Liu, Xiabi

AU - Boumaraf, Said

PY - 2019/7

Y1 - 2019/7

N2 - This paper proposes an effective feature selection method based on monarch butterfly optimization and Fisher criterion. Fisher criterion is applied to evaluate the feature subsets, based on which the optimal feature subsets are searched by using monarch butterfly optimization algorithm. To combine these two components, a method is developed to binarize continuous solution vectors for deciding the feature selection. We conduct experiments on widely used UCI (University of California, Irvine) classification datasets to study the design of our algorithm and compare it with other state-of-the-art counterparts. The experimental results show that the proposed method is reasonable and effective, which achieves the best result of feature selection among the compared methods and has satisfactory efficiency.

AB - This paper proposes an effective feature selection method based on monarch butterfly optimization and Fisher criterion. Fisher criterion is applied to evaluate the feature subsets, based on which the optimal feature subsets are searched by using monarch butterfly optimization algorithm. To combine these two components, a method is developed to binarize continuous solution vectors for deciding the feature selection. We conduct experiments on widely used UCI (University of California, Irvine) classification datasets to study the design of our algorithm and compare it with other state-of-the-art counterparts. The experimental results show that the proposed method is reasonable and effective, which achieves the best result of feature selection among the compared methods and has satisfactory efficiency.

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DO - 10.1109/IJCNN.2019.8852063

M3 - Conference contribution

AN - SCOPUS:85073225509

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2019 International Joint Conference on Neural Networks, IJCNN 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Joint Conference on Neural Networks, IJCNN 2019

Y2 - 14 July 2019 through 19 July 2019

ER -

A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion

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