A Balance Adjusting Approach of Extended Belief-Rule-Based System for Imbalanced Classification Problem

Abstract

The extended belief-rule-based (EBRB) system has become a widely recognized and effective rule-based system in decision-making. The system uses a data-driven method to generate the rule base by transforming each training sample into a rule. Hence, when an EBRB system is applied in an imbalanced classification dataset, the imbalance of training dataset will retain in the generated rule base. More specifically, the number of rules transformed from majority classes will be far greater than the rules transformed from minority classes. This issue usually leads to a sharp decrease in the accuracies of minority classes. This study analyses how the imbalance of training dataset exists in the generated EBRB and then proposes a Balance Adjusting (BA) approach to eliminate the influence of imbalance in the rule base. The BA approach adjusts rule activation weights of all activated rules, and further enhances the competitiveness of rules with higher activation weight during the rule aggregation process of the EBRB system. Several case studies in imbalanced benchmark classification datasets from UCI demonstrate how the use of the BA approach improves the performance of the EBRB system. This study also conducts a series of experiments to validate the improvement of the proposed approach compared with some conventional and recent existing works. The comparison results illustrate that the BA approach is feasible, effective and robust, and it performs well especially in large scale datasets. Moreover, the BA approach can also combine with various rule activation weight calculation methods, which means it might worth to be applied as a generic process before the rule aggregation process of the EBRB system. INDEX TERMS Extended belief-rule-based system, rule activation weight, imbalanced classification problem.

Highly explainable cumulative belief rule-based system with effective rule-base modeling and inference scheme

“…Furthermore, an enhanced MCDRA method was proposed in [66] to demonstrate that the MCDRA method was able to obtain satisfactory rule activation ratios, accuracies, and response time compared with other rule activation methods. Fang et al [21] focused on the use of EBRBS to handle imbalanced classification problems.…”

Construction and Reasoning Approach of Belief Rule-Base for Classification Base on Decision Tree

“…To further prove the effectiveness of the proposed method, the results of the proposed method are also compared with several novel BRB systems. Those approaches for comparison are EBRB [24], SRA-EBRB [27], VP-EBRB [28] and BA-EBRB [29]. The comparison results were listed in Table 5.…”

Enhancing extended belief rule-based systems for classification problems using decomposition strategy and overlap function

“…In recent years, many effective and efficient classifiers [11][15]- [17] were developed for classification problems on the basis of the extended belief rule-based system (EBRBS), which was proposed by Liu et al [18] by embedding belief structures into the rule antecedent of belief rules so that both rule antecedent and consequent can represent uncertain information, e.g., fuzzy uncertainty, random uncertainty, and incomplete uncertainty. Thus, the classifiers based on the EBRBS have important advances in uncertainty information processing and modeling.…”