Fuzzy ID3 algorithm on linguistic dataset by using WABL defuzzification method

In real life, most of information is presented with words. Also, classification is an important issue to make decisions in daily life. Fuzzy logic gives flexibility to handle the imprecise information for computing with words. And, linguistic variables can be defined by using triangular fuzzy numbers given as L-R fuzzy numbers. This study aims to provide a classification approach by using fuzzy ID3 algorithm for linguistic data. In this study, Weighted Averaging Based on Levels (WABL) method, fuzzy c-means, and fuzzy ID3 algorithm are combined. WABL method is used to obtain crisp data set. Then, fuzzy c-means (FCM) algorithm is performed to introduce the shape of the linguistic terms limits and to obtain each membership degree of each linguistic term defined for fuzzy variables in data sets. At last, Fuzzy ID3 algorithm is applied. The rules are generated and the reasoning is done by using Zadeh T-norm/conorm operators. Experimental study is performed on six well-known data sets (Iris, Wdcb, Phoneme, Ring, Sonar, and Pima). As a conclusion, we proposed a fuzzy decision tree classification methodology for linguistic datasets.