Abstract
The micro-extended belief rule-based system (Micro-EBRBS) is an advanced rule-based system and has shown its superior ability in solving big data problems. To overcome the activation rule incompleteness and inconsistency of Micro-EBRBS, a new concept, named activation factor (AF), is introduced to revise the calculation of individual matching degree and, furthermore, an AF-based inference (AFI) method is proposed for improving Micro-EBRBS. A comparative analysis study is conducted using three classification datasets. Results demonstrate that the proposed AFI method can not only improve the accuracy of Micro-EBRBS, but also reduce the number of failed data in the process of rule inference.
Supported by the National Natural Science Foundation of China (Nos. 72001043 and 61773123), the Natural Science Foundation of Fujian Province of China (No. 2020J05122), the Humanities and Social Science Foundation of the Ministry of Education of China (No. 20YJC630188), the Chengdu International Science Cooperation Project (No. 2020-GH02-00064-HZ), and the Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project PGC2018-099402-B-I00.
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Yang, LH., Liu, J., Wang, YM., Wang, H., Martínez, L. (2021). Improving Micro-Extended Belief Rule-Based System Using Activation Factor for Classification Problems. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds) Belief Functions: Theory and Applications. BELIEF 2021. Lecture Notes in Computer Science(), vol 12915. Springer, Cham. https://doi.org/10.1007/978-3-030-88601-1_8
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DOI: https://doi.org/10.1007/978-3-030-88601-1_8
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