Enhancing car evaluation classification through machine learning models and metaheuristic optimization algorithms

Abstract

Car classification, using different machine learning models with optimization frameworks, is done for evaluation in this work. We used different models, namely, Extra Trees, XGBoost, Gaussian Naive Bayes, K-Nearest Neighbors, Histogram-based Gradient Boosting (Hist Gradient Boosting), and Linear Discriminant Analysis, for classification. The car samples are classified as “very good,” “good,” “acceptable,” and “unacceptable.” Among these, Hist Gradient Boosting has the highest value for precision, accuracy, recall, and F1 score. We further tuned this model using Evolutionary Strategies, Evolutionary Programming, Covariance Matrix Adaptation Evolution Strategy, and the Flower Pollination Algorithm. Our outcomes indicate that the Covariance Matrix Adaptation Evolution Strategy and Flower Pollination Algorithm significantly enhanced the performance of the model and outperformed Evolutionary Programming. This work investigates the potential of integrating advanced machine learning models with sophisticated optimization strategies to deliver an effective car evaluation classification process that will be useful in this industry and, perhaps, in many other classification tasks.