Multi-Class Decision-Making Method for Decision-Theoretic Rough Sets Based on the Constructive Covering Algorithm

The constructive covering algorithm (CCA) has unique advantages and functions in dealing with multiple-classification problems. To better address the defects and deficiencies of the existing multi-class decision-theoretic rough sets (DTRSs) model, this paper introduces the CCA into DTRSs and constructs a multi-class DTRSs model based on the CCA. First, through the process of machine learning, this method achieves automatic multi-class clustering and effectively addresses the shortcomings of DTRSs in solving multi-class problems, namely, the large amount of computation and the impact of subjective evaluation factors on the inference of threshold values and decision rules as a result of excessive parameters. Second, by optimizing the covering centre and selecting the covering radius, the proposed method improves the efficiency of machine learning. Third, by setting up a distance parameter between covering classes, the method effectively addresses decision redundancy and conflicts when a DTRSs model is used to solve multi-class problems. Then, by organically integrating the cost sensitivity, inter-class distance parameter and class quality function and systematically re-classifying the objects in the boundary domain, it effectively solves the problem of an excessively large boundary domain. Finally, we summarize the decision-making algorithm of the multi-class DTRSs model based on the CCA and demonstrate the feasibility and effectiveness of the method through experiments.