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 project, 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.

Depending on AutoML frameworks as black-box can leave machine learning practitioners without insights into the inner working of the AutoML process and hence influence their trust in the models produced. In addition, excluding humans from the loop creates several limitations. For example, most of the current AutoML frameworks ignore the user preferences on defining or controlling the search space, which consequently can impact the performance of the models produced and the acceptance of these models by the end-users. The research in the area of transparency and controllability of AutoML has attracted much interest lately, both in academia and industry. How ever, existing tools are usually restricted to supervised learning tasks such as classification and regression, while unsupervised learning, particularly clustering, remains a largely unexplored problem. Motivated by these shortcomings, this project focuses on interactive visualization tool that enables users to refine the search space of AutoML and analyze the results. The project also focuses on meta-learning techniques to recommend a time budget that is likely adequate for a new dataset to obtain well-performing pipeline.

Project Publications:

• R. El Shawi, H. Lekunze , S. Sakr. cSmartML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Clustering. IEEE BigData 2021. link
• R. El Shawi, S. Sakr. cSmartML-Glassbox: Increasing Transparency and Controllability in Automated Clustering. ICDMW 2022.
• R. El Shawi, S. Sakr. TPE-AutoClust: A Tree-based Pipline Ensemble Framework for Automated Clustering. ICDMW 2022. 

Contact Information

Radwa El Shawi

 Radwa [dot] elshawi [at] ut [dot] ee