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Open AccessArticle

Compact Belief Rule Base Learning for Classification with Evidential Clustering

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Author to whom correspondence should be addressed.
The paper is an extended version of our paper published in 2018 International Conference on Information Fusion, Cambridge, UK, 10–13 July 2018.
Entropy 2019, 21(5), 443;
Received: 27 March 2019 / Revised: 24 April 2019 / Accepted: 28 April 2019 / Published: 28 April 2019
(This article belongs to the Special Issue Entropy Based Inference and Optimization in Machine Learning)
PDF [866 KB, uploaded 13 May 2019]


The belief rule-based classification system (BRBCS) is a promising technique for addressing different types of uncertainty in complex classification problems, by introducing the belief function theory into the classical fuzzy rule-based classification system. However, in the BRBCS, high numbers of instances and features generally induce a belief rule base (BRB) with large size, which degrades the interpretability of the classification model for big data sets. In this paper, a BRB learning method based on the evidential C-means clustering (ECM) algorithm is proposed to efficiently design a compact belief rule-based classification system (CBRBCS). First, a supervised version of the ECM algorithm is designed by means of weighted product-space clustering to partition the training set with the goals of obtaining both good inter-cluster separability and inner-cluster pureness. Then, a systematic method is developed to construct belief rules based on the obtained credal partitions. Finally, an evidential partition entropy-based optimization procedure is designed to get a compact BRB with a better trade-off between accuracy and interpretability. The key benefit of the proposed CBRBCS is that it can provide a more interpretable classification model on the premise of comparative accuracy. Experiments based on synthetic and real data sets have been conducted to evaluate the classification accuracy and interpretability of the proposal. View Full-Text
Keywords: rule-based classification; belief function theory; evidential C-means; evidential partition entropy rule-based classification; belief function theory; evidential C-means; evidential partition entropy

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Jiao, L.; Geng, X.; Pan, Q. Compact Belief Rule Base Learning for Classification with Evidential Clustering. Entropy 2019, 21, 443.

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