Fuzzy Decision Rule-Based Online Classification Algorithm in Fuzzy Formal Decision Contexts

Formal concept analysis (FCA) extracts interpretable rules by using implication relationships between concepts, which is an effective method for acquiring knowledge. In this article, a fuzzy decision rule-based online classification algorithm called OFRCA is designed by combining online learning theory and FCA in a fuzzy formal decision context. First, the original weight vector of all attributes is obtained by fusing the original deterministic decision rules. Second, incremental fuzzy decision rules are obtained according to the added objects. Then, for the incremental fuzzy decision rules, OFRCA updates the weight vector based on minimizing the loss function. At the end of the process, all the rules are fused into an attribute-weighted classifier with the final weight vector. In this article, we employ the regret to obtain a learning guarantee for OFRCA. The growth rate of OFRCA's regret converges to 0 as the number of iterations increases in the ideal state, which provides an effective learning guarantee for OFRCA. To verify the effectiveness of the proposed algorithm, this article conducted numerical experiments and systematically analyzed the experimental results, which demonstrated that OFRCA achieved the highest advanced classification performance among the compared algorithms.

Automated summary

This article introduces a fuzzy decision rule-based online classification algorithm called OFRCA, which combines online learning theory and Formal Concept Analysis (FCA) in a fuzzy formal decision context. The algorithm obtains weight vectors for attributes and incremental fuzzy decision rules through a fusion process. The weight vector is updated based on a loss function, and the final attribute-weighted classifier is formed by fusing all the rules. Numerical experiments show that OFRCA achieves the highest advanced classification performance.