A consistency analysis-based rule activation method for extended belief-rule-based systems

Yang, Long-Hao; Wang, Ying‐Ming; Fu, Yang-Geng

doi:10.1016/j.ins.2018.02.059

Cited by 40 publications

(18 citation statements)

References 29 publications

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“…The EBRB system was proposed by Liu et al [14] and has been widely used in various prediction problems [15]. An EBRB system consists of a series of extended belief rules with embedding the belief structure into all attributes, where the kth rule (k = 1, .…”

Section: A Review Of Traditional Ebrb Systemmentioning

confidence: 99%

“…Therefore, to overcome the limitations of the existing studies, this paper proposes an environmental governance cost prediction model based on an extended belief rule-based (EBRB) system and technological constraints, in which the EBRB system was proposed by Liu et al [14] and has demonstrated its excellent prediction performance on many real problems [15]. According to the modeling mechanism of EBRB system, historical input-output data pairs can be used to efficiently generate an extended belief rule base, which is the rule base of the EBRB system, and the basic parameters in the rule base should be optimized to enhance the accuracy of the EBRB system [16].…”

Section: Introductionmentioning

confidence: 99%

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Environmental Governance Cost Prediction of Transportation Industry by Considering the Technological Constraints

Wang

2020

Symmetry

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In order to solve the problem of environmental governance investment planning in the transportation industry, a cost prediction model is proposed under technological constraints, where the input output indictors emphasizes the flexibility of prediction and its characters are asymmetric, while the constructs of prediction model focuses on the standardization and its characters are symmetrical. The basic principle of the cost prediction model is based on an extended belief rule-based (EBRB) system to model the input-output relationship in investment planning, and a parameter learning model to improve the accuracy of the EBRB system. Additionally, the technological innovation factors are also embedded in the cost prediction model to investigate the influence of technology-related outcomes on investment planning. Finally, based on the data of environmental governance in China’s transportation industry from 2003 to 2016, the cost of transportation industry environmental management in China’s thirty provinces from 2017 to 2033 is predicted under the constraints of technological innovation. Results show that: (1) the accuracy of the proposed cost prediction model is higher than some existing cost prediction methods; (2) the predicted environmental governance costs have a significant regional difference; (3) the upgrading of technological innovation is conducive to saving the future environmental governance costs of the transportation industry in some provinces. In addition to the above results, the present study provides model supports and policy references for government decision makers in transportation industry-related environmental cost planning.

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Section: A Review Of Traditional Ebrb Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Environmental Governance Cost Prediction of Transportation Industry by Considering the Technological Constraints

Wang

2020

Symmetry

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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%

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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“…They suggested that the Hamming distance can improve the accuracy of the EBRB system for the binary classification problems. Recently, Yang et al [28] proposed the consistency analysis-based rule activation (CABRA) method to define and select suitable activation rules, where the essence of the CABRA method is a linear optimization model. They showed that the CABRA method can solve the inconsistency problem and also make the conventional EBRB system have a better accuracy than that improved by the DRA method for 9 classification datasets.…”

Section: Applicability Of Conventional Ebrb System To Classification mentioning

confidence: 99%

“…Clearly, Table 1 shows that except for the first rule which has an irrational activation weight, the other rules are all activated for the input data. Although many attempts, including the DRA method [26] and the CABRA method [28], were made to solve the inconsistency problem, they all failed to effectively construct an efficient procedure of activation rule determination for the EBRB system.…”

Section: Drawbacks Of Conventional Ebrb System In Classification Probmentioning

confidence: 99%

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

Yang

Wang

et al. 2018

Applied Soft Computing

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Among many rule-based systems employed to deal with classification problems, the extended belief-rule-based (EBRB) system is an effective and efficient tool and also has potentials in handing both quantitative and qualitative information under uncertainty. Despite many advantages, several drawbacks must be overcome for better applying the conventional EBRB system, including counterintuitive individual matching degrees, insensitivity to the calculation of individual matching degrees, and the inconsistency problem. Accordingly, by constructing the activation region of extended belief rules and revising the calculation formula of activation weights, the new procedures of activation rule determination and weight calculation are proposed to improve the conventional EBRB system, while the original procedures of rule inference and class estimation are retained from the conventional EBRB system. Nineteen classification datasets with different numbers of classes are studied to validate the efficiency and effectiveness of the proposed EBRB classification system compared with existing works. The comparison results demonstrate that the proposed EBRB classification system not only obtains a high accuracy better than the conventional EBRB system, but also has an excellent response time for classification. More importantly, the results derived from multi-class datasets show the significant performance of the proposed EBRB classification system compared with some state of art classification tools.

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A consistency analysis-based rule activation method for extended belief-rule-based systems

Cited by 40 publications

References 29 publications

Environmental Governance Cost Prediction of Transportation Industry by Considering the Technological Constraints

Environmental Governance Cost Prediction of Transportation Industry by Considering the Technological Constraints

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

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

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