Affiliations: [a] School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| [b] Alliance Manchester Business School, The University of Manchester, Manchester, United Kingdom
Correspondence:
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Corresponding author. Shuaiyu Yao, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China. E-mail: [email protected].
Abstract: In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana, Haberman’s survival, and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes.
Keywords: Probabilistic modeling, interpretable inference and classification, maximum
likelihood evidential reasoning (MAKER) framework, belief rule-base, machine learning
