An intuitionistic fuzzy-rough set model and its application to feature selection

Abstract

Due to the development of modern internet-based technology, the electronically stored information is growing exponentially with time. It is highly challenging to select relevant and non-redundant features of the real-valued high dimensional datasets. Feature selection, a preprocessing technique, refers to the process of reducing the dimension of the input data in order to extract the most meaningful features for processing and analysis. One of the numerous useful applications of rough set theory is the attribute or feature selection, but it has certain limitations as it cannot be applied on real-valued data sets directly because rough set based feature selection can handle discrete data only. In order to deal with real-valued data sets, discretization method is applied to convert dataset from real-valued to discrete, which usually leads to information loss. Fuzzy rough set theory is profitably applied to address this problem and retain the semantics of real-valued datasets. However, intuitionistic fuzzy set can deal with uncertainty in a much better way when compared to fuzzy set theory as it considers membership, non-membership and hesitancy degree of an object simultaneously. In this paper, an intuitionistic fuzzy rough set model is established by combining intuitionistic fuzzy set and rough set. Furthermore, we propose a novel approach of feature selection derived from this model. Moreover, we develop an algorithm based on our proposed concept. Finally, our approach is applied to some benchmark data sets and compared with the existing fuzzy rough set based technique. The performed experiments show the superiority of our approach. © 2019 - IOS Press and the authors.