Reinforcement Learning Approach for Parallelization in Filters Aggregation Based Feature Selection Algorithms

TL;DR

This paper proposes parallelization techniques to enhance the performance and feature selection quality of the MeLiF ensemble filter algorithm in machine learning.

Contribution

It introduces two novel parallelization methods to improve MeLiF's efficiency and effectiveness in feature selection tasks.

Findings

Parallelization schemes significantly improve algorithm performance

Enhanced feature selection quality demonstrated through experiments

Faster processing times for large datasets

Abstract

One of the classical problems in machine learning and data mining is feature selection. A feature selection algorithm is expected to be quick, and at the same time it should show high performance. MeLiF algorithm effectively solves this problem using ensembles of ranking filters. This article describes two different ways to improve MeLiF algorithm performance with parallelization. Experiments show that proposed schemes significantly improves algorithm performance and increase feature selection quality.