Optimal meso-granularity selection for classification based on Bayesian optimization

Multi-granularity formal concept analysis is a research field that integrates granular computing and formal concept analysis. It provides valuable opportunities to analyze problems at multiple levels of granularity, which has been becoming a key tool for data mining and knowledge discovery. In multi-granularity formal concept analysis, it is an important challenge to select the optimal granularity. Existing methods for optimal granularity selection typically focus on a single granularity of attributes, without considering meso-granularity layers or the combination of different attribute granularities. Furthermore, these methods fail to reduce redundant attributes effectively. This paper aims to solve this issue and further improve the performance of classification. Specifically, we first introduce the meso-granularity class-attribute blocks to address these limitations. Then, we introduce information gain and design an optimal meso-granularity selection algorithm by combining meso-granularity class-attribute blocks with decision information. Furthermore, we propose the concept of core attributes and define their internal and external significance, which is beneficial for attribute reduction. Finally, the optimal meso-granularity combination is selected by using Bayesian optimization , which improves the efficiency of the meso-granularity combination for classification. The effectiveness of the proposed model is validated through experiments on real-world datasets. The experimental results demonstrate that the proposed model outperforms existing methods, achieving the best classification performance among the compared methods. All codes have been released at https://github.com/yqlinux/OMSFC .