Automated Predictive Model Selection Using Meta-Learning Techniques

Abstract

Recent advances in machine learning have produced numerous predictive algorithms for classification, regression, and forecasting tasks. However, selecting the most suitable model for a specific dataset remains challenging, often requiring expert knowledge, extensive experimentation, and significant computational resources. To address this issue, automated model selection has emerged as an important research area within machine learning and intelligent decision-support systems. Meta-learning, or “learning to learn,” provides an effective solution by utilizing knowledge gained from previously analyzed datasets to predict the performance of learning algorithms on new datasets. It examines dataset characteristics, known as meta-features, and recommends appropriate machine learning models, thereby improving selection accuracy while reducing computational costs. This study proposes a comprehensive meta-learning framework for automated predictive model selection. The framework includes dataset characterization, meta-feature extraction, meta-dataset generation, algorithm evaluation, and meta-model construction. Statistical, information-theoretic, landmarking, and complexity-based features are used to describe datasets and train a meta-learning model capable of recommending suitable algorithms for new predictive tasks. The research evaluates several popular machine learning algorithms, including Decision Trees, Support Vector Machines, Random Forests, Naïve Bayes, Artificial Neural Networks, and k-Nearest Neighbor classifiers. Experimental results demonstrate that meta-learning significantly improves model recommendation accuracy compared to traditional trial-and-error approaches while reducing training time and computational overhead. As part of the broader field of Automated Machine Learning (AutoML), the proposed framework offers an intelligent algorithm recommendation system that supports efficient resource utilization and assists practitioners in selecting high-performing models without extensive machine learning expertise. The findings highlight the potential of meta-learning-based model selection for future intelligent analytics, decision-support, and large-scale data mining systems.