2019/12/18 by Pablo Moscato, Haoyuan Sun, Mohammad Nazmul Haque · 17 citations
Computer Science · Mathematics · #Algorithm #Artificial intelligence #Benchmark (surveying) #Computer science #Evolutionary Algorithms and Applications #Fraction (chemistry) #Generalization #Genetic algorithm #Genetic programming #Machine learning #Mathematics #Memetic algorithm #Metaheuristic Optimization Algorithms Research #Neural Networks and Applications #Regression #Representation (politics) #Statistics #Symbolic regression #cs.LG #cs.NE
paper · pdf · doi:10.1016/j.eswa.2021.115018
published in Expert Systems with Applications 179, 115018 (Elsevier BV) · Submitted to Expert Systems with Applications
arxiv created 2019/12/18 · openalex publication_date 2021/04/20 · arxiv updated 2021/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present an approach for regression problems that employs analytic continued fractions as a novel representation. Comparative computational results using a memetic algorithm are reported in this work. Our experiments included fifteen other different machine learning approaches including five genetic programming methods for symbolic regression and ten machine learning methods. The comparison on training and test generalization was performed using 94 datasets of the Penn State Machine Learning Benchmark. The statistical tests showed that the generalization results using analytic continued fractions provides a powerful and interesting new alternative in the quest for compact and interpretable mathematical models for artificial intelligence.