2025/07/20 by Joanna Komorniczak, Komorniczak, Joanna
Computer Science · #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Neural and Evolutionary Computing (cs.NE) #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.2507.15132
openalex publication_date 2025/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The research community continues to seek increasingly more advanced synthetic data generators to reliably evaluate the strengths and limitations of machine learning methods. This work aims to increase the availability of datasets encompassing a diverse range of problem complexities by proposing a genetic algorithm that optimizes a set of problem complexity measures for classification and regression tasks towards specific targets. For classification, a set of 10 complexity measures was used, while for regression tasks, 4 measures demonstrating promising optimization capabilities were selected. Experiments confirmed that the proposed genetic algorithm can generate datasets with varying levels of difficulty by transforming synthetically created datasets to achieve target complexity values through linear feature projections. Evaluations involving state-of-the-art classifiers and regressors revealed a correlation between the complexity of the generated data and the recognition quality.