Boosted stochastic fuzzy granular hypersurface classifier

In this work, we design a boosted stochastic fuzzy granular hypersurface classifier (BSFGHC) to resolve the classification issue of numerical data and non-numerical data (such as information granules) from the standpoint of granular computing. The scheme is divided into three parts: first, we present an adaptive cluster center clustering (ACCC) algorithm to achieve cluster centers of the data and to realize the fuzzy granulation of data parallelly based on Spark, which dramatically improves the granulation efficiency; second, we build a fuzzy granular space, design various fuzzy granular operators and measurement in the space to construct fuzzy granular hypersurfaces, create the loss function, and employ Particle Swarm Optimization (PSO) to resolve the optimal fuzzy granular hypersurfaces; third, we randomly divide the fuzzy granules to train multiple optimal fuzzy granular hypersurfaces and combine with the classification accuracy of fuzzy hypersurfaces and the difficulty of fuzzy granule subset classification to form a boosted fuzzy hypersurface to predict the data comprehensively. Experimental results and theoretical analysis demonstrate the outstanding performance of the method.