A meta-feature selection method based on the Auto-sklearn framework 

Kulin N.I., Muravyov S.B. A meta-feature selection method based on the Auto-sklearn framework. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 5, pp. 702–708. doi: 10.17586/2226-1494-2021-21-5-702-708

In recent years, the task of selecting and tuning machine learning algorithms has been increasingly solved using automated frameworks. This is motivated by the fact that when dealing with large amounts of data, classical methods are not efficient in terms of time and quality. This paper discusses the Auto-sklearn framework as one of the best solutions for automated selection and tuning machine learning algorithms. The problem of Auto-sklearn 1.0 solution based on Bayesian optimization and meta-learning is investigated. A solution to this problem is presented. A new method of operation based on meta-database optimization is proposed. The essence of the method is to use the BIRCH clustering algorithm to separate datasets into different groups. The selection criteria are the silhouette measure and the minimum number of initial Bayesian optimization configurations. The next step uses a random forest model, which is trained on a set of meta-features and the resulting labels. Important meta-features are selected from the entire set. As a result, an optimal set of important meta-features is obtained, which is used to find the initial Bayesian optimization configurations. The described method significantly speeds up the search for the best machine learning algorithm for classification tasks. The experiments were conducted with datasets from OpenML to compare Auto-sklearn 1.0, 2.0 and a new version that uses the proposed method. According to the results of the experiment and statistical Wilcoxon T-criterion tests, the new method was able to outperform the original versions in terms of time, outperforms Auto-sklearn 1.0 and competes with Auto-sklearn 2.0. The proposed method will help to speed up the time to find the best solution for machine learning tasks. Optimization of such frameworks is reasonable in terms of saving time and other resources, especially when working with large amounts of data.

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