An Improvement of IFS-Based Classification Using Correlation Coefficient between Features

Over the past decades, theoretical and application researches of similarity measures between intuitionistic fuzzy sets (IFSs) have been continuously revealed. Solving pattern classification problems is one of most prominent areas to which these similarity measures can be applied. Differing from other aspect frameworks for classification, IFS-based frameworks do not take relationship among features into account. In the present paper, a modified IFS-based framework by using correlation coefficient among features is presented. The experimental results on various real-world problems show that the proposed framework achieves a satisfactory performance.