Feature Selection for Binary Dataset using Dragonfly Algorithm
Zaid Tariq Raouf, Dhafar Hamed Abd · 2023
In contemporary times, the proliferation of data dimensionality has introduced many challenges within the realm of machine learning. Consequently, identifying and selecting pertinent features have assumed paramount importance. Consequently, a diverse array of techniques for feature selection has been proffered. Among them, Metaheuristic techniques are particularly significant within this milieu, with a focus on the dragonfly algorithm garnering notable attention. Metaheuristic algorithms are rooted in the emulation of swarm intelligence, drawing inspiration from the collective behaviors exhibited by insects. This scholarly inquiry aims to enhance the efficacy of classification outcomes by leveraging the dragonfly algorithm to select the most salient features. In this paper, the proposed approach is rigorously evaluated across three distinct datasets, namely, “Ansur,” “Predict 5-Year Career Longevity for NBA Rookies,” and “Chronic Kidney Disease,” all of which were procured from the Kaggle repository. The study used four machine learning classifiers, namely, XGBoosting, K-Nearest Neighbors (KNN), Decision Tree, and Gaussian Naive Bayes (Gaussian-NB), for classification and evaluation. The empirical findings unveiled a notable enhancement in classification performance. Notably, the accuracy metrics exhibit substantial improvements across all classifiers. For instance, within chronic kidney disease classification, both XGBoost and Decision Tree classifiers yielded a remarkable accuracy rate of 100%, thereby underscoring the efficacy of the proposed dragonfly algorithm-based feature selection technique in augmenting the predictive capabilities of machine learning models.