Boosted Fuzzy Granular Regression Trees

Abstract

The regression problem is a valued problem in the domain of machine learning, and it has been widely employed in many fields such as meteorology, transportation, and material. Granular computing (GrC) is a good approach of exploring human intelligent information processing, which has the superiority of knowledge discovery. Ensemble learning is easy to execute parallelly. Based on granular computing and ensemble learning, we convert the regression problem into granular space equivalently to solve and proposed boosted fuzzy granular regression trees (BFGRT) to predict a test instance. The thought of BFGRT is as follows. First, a clustering algorithm with automatic optimization of clustering centers is presented. Next, in terms of the clustering algorithm, we employ MapReduce to parallelly implement fuzzy granulation of the data. Then, we design new operators and metrics of fuzzy granules to build fuzzy granular rule base. Finally, a fuzzy granular regression tree (FGRT) in the fuzzy granular space is presented. In the light of these, BFGRT can be designed by parallelly combing multiple FGRTs via random sampling attributes and MapReduce. Theory and experiments show that BFGRT is accurate, efficient, and robust.