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An adjustable fuzzy classification algorithm using an improved multi-objective genetic strategy based on decomposition for imbalance dataset

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Abstract

In this paper, we propose an adjustable fuzzy classification algorithm using multi-objective genetic strategy based on decomposition (AFC_MOGD) to solve imbalance classification problem. In AFC_MOGD, firstly, an improved multi-objective genetic strategy based on decomposition is adopted as the basic optimization algorithm in which a new updating pattern getting good solutions is designed. Then, an adjustable parameter which is ranged in the interval [0, 1] is used to adjust complexity of each classifier artificially. Finally, a normalized method which takes class percentage into account to determine class label and rule weight of each rule is introduced so as to obtain more reasonable rules. The proposed algorithm is compared with three typical algorithms on eleven imbalance datasets in terms of area under the ROC of convex hull. The Wilcoxon signed-rank test is also carried out to show that our algorithm is superior to other algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61876141, 61373111, 61272279, 61103119, 61672405 and 61203303); the Fundamental Research Funds for the Central Universities (No. JB1502027); and the Provincial Natural Science Foundation of Shaanxi of China (No. 2014JM8321).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, P.O. Box 224, No. 2, South Taibai Road, Xi’an, 710071, People’s Republic of China

    Ruochen Liu, Fangfang Wang & Licheng Jiao

  2. School of Computer, Xi’an Shiyou University, Xi’an, 710065, People’s Republic of China

    Manman He

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  1. Ruochen Liu
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  2. Fangfang Wang
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  4. Licheng Jiao
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Correspondence to Ruochen Liu.

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Liu, R., Wang, F., He, M. et al. An adjustable fuzzy classification algorithm using an improved multi-objective genetic strategy based on decomposition for imbalance dataset. Knowl Inf Syst 61, 1583–1605 (2019). https://doi.org/10.1007/s10115-019-01342-5

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