Multi-Objective Evolutionary approach for the Performance Improvement of Learners using Ensembling Feature selection and Discretization Technique on Medical data

TL;DR

This paper introduces a multi-objective genetic algorithm framework that combines feature selection and discretization to improve the performance of machine learning models on biomedical data by reducing data redundancy and dimensionality.

Contribution

It presents a unified ensemble approach integrating feature selection and discretization using NSGA-II, addressing the fragmentation in preprocessing techniques for biomedical data.

Findings

Enhanced feature selection accuracy with multi-objective optimization

Reduced data dimensionality effectively through combined discretization and feature reduction

Improved classification performance on biomedical datasets

Abstract

Biomedical data is filled with continuous real values; these values in the feature set tend to create problems like underfitting, the curse of dimensionality and increase in misclassification rate because of higher variance. In response, pre-processing techniques on dataset minimizes the side effects and have shown success in maintaining the adequate accuracy. Feature selection and discretization are the two necessary preprocessing steps that were effectively employed to handle the data redundancies in the biomedical data. However, in the previous works, the absence of unified effort by integrating feature selection and discretization together in solving the data redundancy problem leads to the disjoint and fragmented field. This paper proposes a novel multi-objective based dimensionality reduction framework, which incorporates both discretization and feature reduction as an ensemble model for performing feature selection and discretization. Selection of optimal features and the categorization of discretized and non-discretized features from the feature subset is governed by the multi-objective genetic algorithm (NSGA-II). The two objective, minimizing the error rate during the feature selection and maximizing the information gain while discretization is considered as fitness criteria.