TY - JOUR
T1 - Competition-Driven Multimodal Multiobjective Optimization and Its Application to Feature Selection for Credit Card Fraud Detection
AU - Han, Shoufei
AU - Zhu, Kun
AU - Zhou, Mengchu
AU - Cai, Xinye
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62071230 and Grant 62061146002; in part by the Natural Science Foundation of Jiangsu Province under Grant BK20211567; and in part by the Fundo para o Desenvolvimento das Ciencias e da Tecnologia (FDCT) under Grant 0047/2021/A1.
Publisher Copyright:
© 2013 IEEE.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Feature selection has been considered as an effective method to solve imbalanced classification problems. It can be formulated as a multiobjective optimization problem (MOP) aiming to find a small feature subset while achieving a high classification accuracy. With traditional MOP, the focus is on deriving an optimal solution (i.e., a feature subset), while ignoring the diversity in solution space (e.g., there could exist multiple feature subsets achieving the same accuracy). Providing more options for feature selection would be beneficial since some features can be more difficult to obtain than others. In this work, we treat feature selection as a multimodal MOP (MMOP) whose goals are to find an excellent Pareto front in objective space and as many equivalent Pareto optimal solutions (feature subsets) as possible in feature space. Note that though several multimodal multiobjective evolutionary algorithms (MMEAs) have been proposed, their use of a convergence-first selection criterion could cause the loss of solution diversity in an objective and feature space. To address the issue, a novel competition-driven mechanism is designed to assist the existing multimodal MMEAs in locating more equivalent feature subsets and a desired Pareto front. The effectiveness of the proposed mechanism is first verified on all 22 MMOPs from CEC2019. Then, the proposed method is applied to feature selection in imbalanced classification problems and a real-world application, i.e., credit card fraud detection. Experimental results show that the proposed mechanism can not only provide more equivalent feature subsets but also improve classification accuracy.
AB - Feature selection has been considered as an effective method to solve imbalanced classification problems. It can be formulated as a multiobjective optimization problem (MOP) aiming to find a small feature subset while achieving a high classification accuracy. With traditional MOP, the focus is on deriving an optimal solution (i.e., a feature subset), while ignoring the diversity in solution space (e.g., there could exist multiple feature subsets achieving the same accuracy). Providing more options for feature selection would be beneficial since some features can be more difficult to obtain than others. In this work, we treat feature selection as a multimodal MOP (MMOP) whose goals are to find an excellent Pareto front in objective space and as many equivalent Pareto optimal solutions (feature subsets) as possible in feature space. Note that though several multimodal multiobjective evolutionary algorithms (MMEAs) have been proposed, their use of a convergence-first selection criterion could cause the loss of solution diversity in an objective and feature space. To address the issue, a novel competition-driven mechanism is designed to assist the existing multimodal MMEAs in locating more equivalent feature subsets and a desired Pareto front. The effectiveness of the proposed mechanism is first verified on all 22 MMOPs from CEC2019. Then, the proposed method is applied to feature selection in imbalanced classification problems and a real-world application, i.e., credit card fraud detection. Experimental results show that the proposed mechanism can not only provide more equivalent feature subsets but also improve classification accuracy.
KW - Competition mechanism (CM)
KW - credit card fraud detection
KW - feature selection
KW - imbalanced classification
KW - machine learning
KW - multimodal MOPs (MMOPs)
KW - multimodal multiobjective evolutionary algorithms (MMEAs)
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U2 - 10.1109/TSMC.2022.3171549
DO - 10.1109/TSMC.2022.3171549
M3 - Article
AN - SCOPUS:85142744519
SN - 2168-2216
VL - 52
SP - 7845
EP - 7857
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 12
ER -