Adaptive neighborhood granularity selection and combination based on margin distribution optimization

Granular computing aims to develop a granular view for interpreting and solving problems. The model of neighborhood rough sets is one of effective tools for granular computing. This model can deal with complex tasks of classification learning. Despite the success of the neighborhood model in attribute reduction and rule learning, it still suffers from the issue of granularity selection. Namely, it is an open problem to select a proper granularity of neighborhood for a specific task. In this work, we explore ensemble learning techniques for adaptively evaluating and combine the models derived from multiple granularity. In the proposed framework, base classifiers are trained in different granular spaces. The importance of base classifiers is then learned by optimizing the margin distribution of the combined system. Experimental analysis shows that the proposed method can adaptively select a proper granularity, and combining the models trained in multi-granularity spaces leads to competent performance.