Applying Dragonfly Algorithm for Feature Selection Optimizing in Machine Learning Classification

Feature selection plays a crucial role in the domain of machine learning, serving as an essential mission. Eliminating redundant and irrelevant attributes can enhance the learning process by increasing the learning rate, improving accuracy, and enhancing classifier performance. The primary objectives of feature selection encompass the creation of models that are more understandable and user-friendly, the enhancement of data mining efficiency, and the facilitation of data cleaning and organization for subsequent analysis. Metaheuristic algorithms have garnered considerable attention due to their prowess in solving many optimization problems such as feature section, in this paper using Dragonfly algorithm. The effectiveness of the proposed methodologies is evaluated on three benchmark datasets obtained from the Kaggle repository: the heart disease dataset, the Vehicle-Coupon-Recommendation dataset, and the Predict Diabetes dataset. Three machine learning classifiers—namely, Light Gradient Boosting Machine (LightGBM), AdaBoost, and Bagging—are utilized in this assessment. This approach yields an impressive accuracy rate of 85.71%. In contrast, when all features are included in the training process, the accuracy drops to a lower value of 83.51%.