research-article

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

Authors:

Licheng JiaoAuthors Info & Claims

Volume 61, Issue 3

Pages 1583 - 1605

https://doi.org/10.1007/s10115-019-01342-5

Published: 01 December 2019 Publication History

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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Cited By

Wang MWang SGuo JJia W(2023)Improving stock trend prediction with pretrain multi-granularity denoising contrastive learningKnowledge and Information Systems10.1007/s10115-023-02006-166:4(2439-2466)Online publication date: 28-Dec-2023
https://dl.acm.org/doi/10.1007/s10115-023-02006-1

Index Terms

An adjustable fuzzy classification algorithm using an improved multi-objective genetic strategy based on decomposition for imbalance dataset
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 61, Issue 3

Dec 2019

579 pages

ISSN:0219-1377

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Copyright © 2019 Springer-Verlag London Ltd., part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2019

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Cited By

Wang MWang SGuo JJia W(2023)Improving stock trend prediction with pretrain multi-granularity denoising contrastive learningKnowledge and Information Systems10.1007/s10115-023-02006-166:4(2439-2466)Online publication date: 28-Dec-2023
https://dl.acm.org/doi/10.1007/s10115-023-02006-1

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