Extended belief-rule-based system with new activation rule determination and weight calculation for classification problems

Yang, Long-Hao; Li, Jun; Wang, Ying‐Ming; Martínez, Luis

doi:10.1016/j.asoc.2018.08.004

Cited by 19 publications

(7 citation statements)

References 34 publications

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“…For future work, we will focus on how to combine A-SMOTE with the rough set theory to solve imbalanced datasets classification problem. In addition to that, the problem of imbalanced data has been much related with extended belief rulebased system [53,54] developed to deal with classification tasks. It will have an invaluable contribution in the field of complex data analysis, which we plan to work on in the future.…”

Section: Discussionmentioning

confidence: 99%

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

Hussein¹,

Li²,

Yohannese³

et al. 2019

IJCIS

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Imbalance learning is a challenging task for most standard machine learning algorithms. The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing approach for handling imbalanced datasets, where the minority class is oversampled by producing synthetic examples in feature vector rather than data space. However, many recent works have shown that the imbalanced ratio in itself is not a problem and deterioration of the model performance is caused by other reasons linked to the minority class sample distribution. The blind oversampling by SMOTE leads to two major problems: noise and borderline examples. Noisy examples are those from one class located in the safe zone of the other. Borderline examples are those located in the neighborhood of the class boundary. These samples are associated with deteriorating performance of the models developed. Therefore, it is critical to concentrate on the minority class data structure and regulate the positioning of the newly introduced minority class samples for better performance of classifiers. Hence, this paper proposes the advanced SMOTE, denoted as A-SMOTE, to adjust the newly introduced minority class examples based on distance to the original minority class samples. To achieve this objective, we first employ the SMOTE algorithm to introduce new samples to the minority and eliminate those examples that are closer to the majority than the minority. We apply the proposed method to 44 datasets at various imbalance ratios. Ten widely used data sampling methods selected from the literature are employed for performance comparison. The C4.5 and Naive Bayes classifiers are utilized for experimental validation. The results confirm the advantage of the proposed method over the other methods in almost all the datasets and illustrate its suitability for data preprocessing in classification tasks.

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Section: Discussionmentioning

confidence: 99%

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

Hussein¹,

Li²,

Yohannese³

et al. 2019

IJCIS

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“…From the previous studies on EBRB decision model [25], the consistency among activated rules is a crucial influence to weaken the classification accuracy of an EBRB decision model. Hence, the consistency is taken into consideration in the inference result of EBRB decision model.…”

Section: Consistency-based Ensemble Ebrb Inferencementioning

confidence: 99%

“…Suppose that ARs is the set of activated rules for the sth base EBRB, Dn is the nth (n=1, ..., N) consequent of the consequent attribute and βn,k is the belief degree of the kth activated rule on the nth consequent. The consistency of activated rules ARs is defined as: (24) where Cn is given by: (25) Taking a binary classification problem with two classes {D1, D2} for example, suppose the number of activated rules ARs having the maximum belief degree on D1 and D2 is 8 and 2, namely |ARs|=10, C1=8 and C2=2, the consistency of activate rules ARs is therefore calculated by C(ARs)=8/10=0.8. Definition 2 (Consistency-based integrated belief degree).…”

Section: Consistency-based Ensemble Ebrb Inferencementioning

confidence: 99%

An ensemble extended belief rule base decision model for imbalanced classification problems

Yang

Ren

et al. 2022

Knowledge-Based Systems

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“…To demonstrate the advancement of the BA approach, this study compares it with CABRA-EBRB [35] and NP-EBRB [36], both of them are novel and significant works proposed in the recent two years. The statistics on the extra datasets used in this section is summarized in Table 8.…”

Section: B Compare With Recent Work Of Ebrb Systemmentioning

confidence: 99%

“…Additionally, the performance of the BA approach in large scale datasets whose sizes are greater than 5000 is compared with those listed in [36], where CABRA-EBRB did not involve in that comparison because it is too time-consuming. Their comparison results are listed in Table 10.…”

Section: B Compare With Recent Work Of Ebrb Systemmentioning

confidence: 99%

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

et al. 2020

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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.

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Extended belief-rule-based system with new activation rule determination and weight calculation for classification problems

Cited by 19 publications

References 34 publications

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

An ensemble extended belief rule base decision model for imbalanced classification problems

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

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