Decision Tree Classifiers Based on Granular Computing
Hongbing Liu *
School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
Fan Zhang
School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
Chang-An Wu
School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
*Author to whom correspondence should be addressed.
Abstract
Bottle-up and top-down are two main computing models in granular computing (GrC). The bottle-up granular computing is used to form decision tree classifiers, or DTCGrC for short. Algorithm DTCGrC constructs a framework of granular computing by the bottle-up join operation which maps all the training data into the granule set, and the achieved granule set is used to form the decision tree classifiers. We compare the performance of DTCGrC with decision tree classifiers, for a number of two-class problems and multiclass problems. Our computational experiments showed that DTCGrC improves the generalization abilities.
Keywords: Decision tree classifier, granular computing, hypersphere granule.