An intuitionistic fuzzy bireduct model and its application to cancer treatment

Abstract

Due to technological advancement, data size has seen a significant increase both in terms of features and instances. An efficient way to handle large sized datasets is to apply data reduction technique to ease the computational intractability of learning algorithms. Intuitionistic fuzzy rough and fuzzy rough sets based approaches provide a handful of solution, but the applicability is mostly restricted to reduction in terms of feature size. However, reduction in terms of instance size has not been handled by any of the existing intuionistic fuzzy set approaches. The present paper introduces the notion of bireducts in intuitionistic fuzzy framework that can be used for simultaneous reduction of instances and features. A robust lower approximation formulation is employed along with laying the foundation for the variants of instance selection technique. The proposed model is therefore robust to noise and can very effectively handle uncertainities due to judgement as well as identification. Further, an efficient instance selection technique in bireduct formulation enhances the performance. The experimental evaluation on benchmark datasets demonstrates the applicability and robustness of the proposed bireducts. It significantly reduces data size both in terms of instances and features whilst maintaining high performances. Further, the model is applied in the challenging domain of cancer treatment by enhancing the prediction performance of anti-angiogenic peptides. A comparative analysis demonstrates the superiority of the proposed methodology. © 2022