A novel (alpha, beta)-indiscernibility-assisted intuitionistic fuzzy-rough set model and its application to dimensionality reduction

An intuitionistic fuzzy (IF) set is an interesting extension of a fuzzy set (FS) that includes more accurate expression of the uncertainty in contrast to conventional FS theory. By considering positive, negative, and hesitancy degree concurrently, IF set appears as an effective notion to deal with the uncertainty included by both the judgement and identification. In this article, we propose an innovative IF rough set (IFRS) framework that integrates IF set and rough set (RS) with the concept of (alpha, beta)-indiscernibility, which incorporates two thresholds to avoid noise. In addition, we utilize the supporting proofs to defend the suggested framework. Thereafter, a feature selection method is presented by using the proposed framework, and an appropriate algorithm is provided to determine the reduct for the high-dimensional datasets. Moreover, a concrete illustration using a sample information system depicts the efficacy of the suggested idea. Then, the proposed method is applied on the benchmark datasets to produce the reduced datasets. Furthermore, performance measures of numerous machine learning techniques are evaluated over these reduced datasets. Finally, a comparative study with the recently discussed fuzzy rough set (FRS) and IFRS-assisted methods is presented to justify the superiority of the established technique.