期刊
KNOWLEDGE-BASED SYSTEMS
卷 223, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2021.107010
关键词
Extended Belief Rule Base; Cascade model; Imbalanced data
资金
- National Natural Science Foundation of China [61773123, 62072110]
- Natural Science Foundation of Fujian Province, China [2019J01647]
This study proposes a Belief Rule-Based (BRB) reasoning model based on the evidential reasoning algorithm to address the issue of data imbalance in rule-based systems. By incorporating data-driven characteristics and data class rebalancing techniques, the model dynamically adjusts rule weights to improve classification performance by expanding the proportion of minority classes and building an ensemble classifier with excellent performance.
In recent years, data imbalance in the conventional classification problem has raised great interest in the industry. However, concerning the rule-based systems, this problem has been rarely investigated. We propose a Belief Rule-Based (BRB) reasoning model based on the evidential reasoning algorithm to fill in the gap by solving the problem caused by imbalanced data with regards to the rule-based systems. It utilizes the data-driven characteristics of Extended Belief Rule-Based (EBRB) and incorporates the data class rebalancing technique with the ensemble learning method of EBRB. In order to increase the number of rules that can promote the correct classification of minority classes in EBRB, we apply an adaptive data resampling strategy to expand the proportion of minority classes. On this basis, the data is divided into several EBRB base learners with different classification characteristics. During the iterative process, the rule weight of the base learner is dynamically adjusted according to the correct and incorrect classification, such that the classifier can pay more attention to the minority classes and reduce the bias of classification. Finally, the optimized EBRB base learner is selected to build an ensemble classifier with excellent classification performance. Experiments on 27 binary class and 12 multi-class imbalanced datasets show that our approach significantly improves F-value and MACC compared with other imbalanced classification algorithms. By building a cascaded EBRB model, the activation weight is taken as the basis of model evolution, the application limitations of the EBRB model in the ensemble learning are solved innovatively, and the classification performance of the EBRB model for imbalanced data is improved significantly. (C) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据