A new approach for attribute reduction from decision table based on intuitionistic fuzzy topology

Most of the current attribute reduction methods use the measure to define the reduct, such as the positive region of rough set theory (RS), information entropy, and distance. However, the size of the reduct based on the measures is still limited. To cope with this problem, we propose a new approach of attribute reduction based on using the intuitionistic fuzzy topology (IFT). Firstly, a new IFT structure based on the pre-order relation and the intuitionistic fuzzy base (IF-base) structure is introduced. Secondly, a new measure is proposed to evaluate the significance of the attribute based on the IF subbase. Finally, the new reduction algorithms based on the IF-base filter and filter-wrapper methods are presented. The theoretical and experimental results show that the proposed method is efficient in terms of size and accuracy of the reduct. Specifically, the reduct of the F_IFT algorithm has an average size of 50% smaller, and the FW_IFT algorithm has an average accuracy of 10% greater than those of the related algorithms. Significantly, the algorithm FW_IFT can very remove noisy attributes. The classification accuracy of the reduct is 15% higher than that of the original set of attributes.