Hybrid Decision Tree and Genetic Algorithm for Enhanced Classification Accuracy

This paper presents a hybrid classifier that leverages the strengths of decision trees and genetic algorithms to improve classification accuracy. The classifier was implemented in Java using object-oriented design principles, resulting in a modular and maintainable code structure. The system consists of four main packages: data management, decision tree construction, genetic algorithm operations, and a graphical user interface. The genetic algorithm optimizes feature selection, which enhances the performance of the decision tree classifier by focusing on relevant features and eliminating noise. Results from experiments showed that the hybrid technique outperformed manual feature selection in three classification tasks: wine quality evaluation, water potability determination, and horse blood sample categorization. The hybrid algorithm's increased accuracy compensates the extra computing expense, despite its longer execution time. To improve feature evaluation, future research will examine new feedback mechanisms from the ID3 algorithm, optimize neural networks, integrate parallel computing, and conduct additional testing.