Prototype-Based Soft Feature Selection Package

Abstract

This paper presents a prototype-based soft feature selection package (Sofes) wrapped around the highly interpretable Matrix Robust Soft Learning Vector Quantization (MRSLVQ) and the Local MRSLVQ algorithms. The process of assessing feature relevance with Sofes aligns with a comparable approach established in the Nafes package, with the primary distinction being the utilization of prototype-based induction learners influenced by a probabilistic framework. The numerical evaluation of test results aligns Sofes’ performance with the Nafes package's.