cSmartML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Clustering
Radwa Elshawi, Hudson Taylor Lekunze, Sherif Sakr · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Novel technologies in automated machine learning ease the complexity of algorithm selection and hyper-parameter optimization. However, these are usually restricted to supervised learning tasks such as classification and regression, while unsupervised learning remains a largely unexplored problem. In this paper, we offer a solution for automating machine learning specifically for the case of unsupervised learning with clustering, in a domain-agnostic manner. This is achieved through a combination of state-of-the-art processes based on meta-learning for algorithm and evaluation criteria selection, and evolutionary algorithm for hyper-parameter tuning. We introduce a robust and scalable interactive tool, named cSmartML, built on scikit-learn with 8 clustering algorithms. In order to capture more than a single measure of goodness of the output clustering solution, cSmartML optimizes multiple objective functions. A pareto-approach evaluates each objective simultaneously for each clustering solution. On each of the 27 real and synthetic benchmark datasets, we show that the performance of cSmartML is often much better than using standard selection and hyper-parameter optimization methods. In addition, experimentation reveals that cSmartML takes advantage of the defined objective functions on multi-objective functions framework.