Enhancing Feature Selection in High-Dimensional Data With Fuzzy Fitness-Integrated Memetic Algorithms

Abstract

In environments rich in data, machine learning models often encounter challenges such as data sparsity and overfitting, primarily due to datasets with an excessive number of features. To address these issues, this paper introduces a novel feature selection method employing a Memetic Algorithm (MA) enhanced with a fuzzy fitness function. This method is articulated in three variations: the Fuzzy Fitness Memetic Algorithm with Tabu Search (FFMATS), the Fuzzy Fitness Memetic Algorithm with Hill Climbing (FFMAHC), and a hybrid that combines both techniques, each utilizing specific local search strategies to refine feature selection. When tested across 16 UCI datasets using four different classifiers, these algorithms not only demonstrated competitive accuracy but frequently outperformed existing methods. These results highlight the critical importance of customizing feature selection strategies to meet the specific needs of various datasets and classifiers, ultimately enhancing the practicality and effectiveness of machine learning models.

Keywords

• Evolutionary algorithms
• feature selection
• fuzzy fitness functions
• local search algorithms
• machine learning
• memetic algorithms