A Machine-learning and Discrete Multi-verse-optimizer-based Hybrid Method for Feature Selection

Accurately identifying key quality features can significantly streamline the manufacturing process by reducing the number of controlled variables and enhancing product quality prediction. To maximize prediction accuracy while minimizing the number of features, this study proposes a Machine-learning and Discrete Multi-verse-optimizer-based Hybrid method called MDMH. MDMH combines machine learning and the discrete Multi-verse optimizer, utilizing hierarchical clustering for effective feature grouping and employing a wrapper technique to find the optimal solution. After comparing its results with those of the exact solver and other intelligent optimization methods, the proposed method demonstrates superiority in terms of accuracy.