IRIS Data Classification Using Tolerant Rough Sets1

Abstract

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data points is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of the optimal similarity threshold value is very important for accurate classification, so we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements so (1) some tolerant objects are required to be included in the same class as many as possible and (2) some objects in the same class are required to be tolerable as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and data set is grouped into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method where all data is classified by using the lower approximation at the first stage and then the nonclassified data at the first stage is classified again by using the rough membership functions obtained from the upper approximation set. The validity of the proposed classification method is tested by applying it IRIS data classification and its classification performance and processing time are compared to those of other classification methods such as BPNN, OFUNN, and FCM.