A rule activation method for extended belief rule base based on improved similarity measures

doi:10.3969/j.issn.0253-2778.2018.01.003

Abstract

Abstract: When calculating negative individual matching degrees, there might appear negative values and all rules’ activation weights may be equal to zero. To address this problem, this paper introduces the Euclidean distance which is based on attribute weights and improves the traditional similarity computational formula. In addition, the traditional rule activation method activates all rules whose activation weights are greater than zero without considering inconsistency which exists in the activated rules, since the inconsistency of activated rules will weaken the reasoning performance of EBRB systems. Hence, considering the inconsistency existing in the activated rules, a new rule activation method of EBRB based on improved similarity measures is proposed. Compared with traditional rule activation method in the EBRB, the proposed approach activates rules by setting thresholds. And these activated rules are not only greater than zero but also have the smallest inconsistency. Finally, the pipeline leak detection problem and multiple public classification datasets have been employed to validate the efficiency of the new rule activation method. The experimental results show that the proposed method based on improved similarity measures can improve the reasoning accuracy of EBRB systems.

Key words: extended belief rule base, rule inconsistency, similarity measures, rule activation method

CLC Number:

TP391

LIN Yanqing, FU Yanggeng. A rule activation method for extended belief rule base based on improved similarity measures[J]. Journal of University of Science and Technology of China, 2018, 48(1): 20-27.

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