Performance Analysis and Evaluation of Fuzzy Pattern Tree Induction

Abstract

Machine learning, data mining, and several related research areas are concerned with methods for the automated induction of models and the extraction of patterns from realistic data. Fuzzy classification is one of the important applications in fuzzy set where values ranges between 0 and 1 .Main objective of the fuzzy logic and fuzzy set to find a set of fuzzy rules that form a classification model. The set is major advantage of using fuzzy rules for classification applications to maintain transparency as well as high accuracy rate. Fuzzy pattern tree induction introduced as a novel for machine learning method for classification. Pattern tree is a hierarchical tree-like structure, whose inner nodes are marked with generalized (fuzzy) logical operators and whose leaf nodes are associated with fuzzy predicates on input attributes. Pattern tree make use of different aggregation including t-norms and t-conorms. There are two types of Fuzzy Pattern trees: (i) Bottom up induction: Information propagates from bottom to top. (ii) Top down induction: Information propagates from top to down. In this paper performance analysis and comparative study of above both approaches using number of parameters like accuracy rate, model size is mentioned. The paper is divided into five sections: section 1 is introduction of Fuzzy pattern tree, section 2 describes types of fuzzy pattern tree schemes and section 3 presents their comparative performance, section 4 does the conclusion. Section 5 includes future scope.