On Distance-Based Attribute Reduction With , -Level Intuitionistic Fuzzy Sets

Attribute reduction, often referred to as feature selection, is a vital step in data preprocessing aimed at eliminating unnecessary attributes and enhancing the efficiency of classification models. Intuitionistic fuzzy sets are widely acknowledged for their highly effective approach to the attribute reduction problem in decision tables, especially in the context of noisy decision tables with low classification accuracy. Nonetheless, the computational complexity of this approach increases significantly due to the additional incorporation of a non-membership component when calculating the significance measures of each attribute. Besides, some intuitionistic fuzzy equivalence classes may include objects with relatively low similarity or high diversity degrees generated from noisy data. Performing calculations on these objects can be time-consuming and lead to inefficiencies in the attribute reduction process. To address the aforementioned issue, we first define an alpha, beta -level set. This allows us to eliminate the mentioned noisy objects from the intuitionistic fuzzy equivalence classes and transform them into alpha, beta -level intuitionistic fuzzy equivalence classes. Subsequently, we construct a distance measure between two alpha, beta -level intuitionistic fuzzy partitions and define a new reduct to preserve the distance between two alpha, beta -level intuitionistic fuzzy partitions generated by the condition attribute set and the decision attribute set. Finally, we propose a novel and efficient heuristic attribute reduction algorithm to find the new reduct, in which we also use a significance measure based on the alpha, beta -level intuitionistic fuzzy partition distance to determine the vital attribute at each step of the algorithm. Obviously, our algorithm is also applied to various benchmark datasets for comparative analysis against existing algorithms. The experimental results demonstrate that the proposed algorithm not only improves the accuracy of the classification model but also significantly reduces execution time when compared to intuitionistic fuzzy rough set-based algorithms applied to noisy datasets with high dimensionality.