Incremental fuzzy rough sets based feature subset selection using fuzzy min-max neural network preprocessing

Fuzzy rough sets (FRS) provides effective ways for selecting the compact/relevant feature subset for hybrid decision systems. However, the underlying complexity of the existing FRS methods through batch processing is often costly or intractable on large data and also suffer from continuous model adaptation on dynamic data. This paper proposes a FRS based incremental feature subset selection (IvFMFRS) framework using fuzzy min-max neural network (FMNN) as a preprocessor step in aiding to deal with data dynamically without sacrificing classification performance. FMNN is a single epoch learning algorithm employed to construct fuzzy hyperboxes (information granules) of pattern spaces very fast. Fuzzy hyperboxes facilitate the formation of interval-valued decision system (IDS) from the numerical decision system of much smaller size. In IvFMFRS, on each sample subset arrival, an incremental mechanism for updating fuzzy discernibility matrix (FDM) based on constructed IDS is first formulated and then update feature subset by adding and deleting features based on updated FDM. A comparative analysis has been conducted comprehensively to assess the performance of the proposed algorithm with the existing FRS methods on numerical datasets. And, the results show that the IvFMFRS obtained the relevant feature subsets with similar classification accuracy with significantly less computational time than existing FRS methods. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces an incremental feature subset selection framework based on fuzzy rough sets, using fuzzy min-max neural network as a preprocessor to handle dynamic data without sacrificing classification performance.