An efficient classification algorithm based on pattern range tree prototypes

Abstract

Abstraction based Pattern Classifier has drawn a lot of attention today. This type of classifier has two phases. They are: design phase, where the abstractions are created and classification phase, where the classification is done using these abstractions. Techniques like neural networks, genetic algorithms require very high design time. In other techniques like nearest neighbor classifier, the design time is near to zero but the classification time is predominantly high. Pattern Count Tree (PC- tree) based classifier was proposed as an abstraction based classifier that strikes a balance between the design time and the classification time. In this paper, we are going to propose a novel data structure called Pattern Range Tree (PR-tree) and a pattern classifier based on PR-tree. Experimental results presented in this paper show that PR-tree based classifier (PRC) is more efficient than PC-tree based classifier (PCC) in terms of storage space, processing time and classification accuracy. � 2007 IEEE.