A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

Attribute reduction, also known as feature selection, for decision information systems is one of the most pivotal issues in machine learning and data mining. Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problem of attribute reduction. Unfortunately, the intuitionistic fuzzy sets based methods have not received much interest, while these methods are well-known as a very powerful approach to noisy decision tables, i.e., data tables with the low initial classification accuracy. Therefore, this paper provides a novel incremental attribute reduction method to deal more effectively with noisy decision tables, especially for high-dimensional ones. In particular, we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions. It should be noted that the intuitionistic fuzzy partition distance is well-known as an effective measure to determine important attributes. More interestingly, an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects. This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables. Besides, some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.