AUTOMATIC HYPERPARAMETER OPTIMIZATION FOR CLUSTERING ALGORITHMS WITH REINFORCEMENT LEARNIN

Abstract

Subject of Research. The paper deals with research of clustering algorithms for hyperparameters optimization used in machine learning. Model selection problem is comprehensively studied, and the need of the tradeoff between exploration and exploitation is identified. Thus, the problem is reduced to multi-armed bandit problem. Method. The paper presented the approach for simultaneous algorithm selection and hyperparameters optimization. We used solution of the Multiarmed Bandit problem and considered Softmax- and UCB1-based algorithm variants in combination with different reward functions. Main Results. Experiments on various datasets from UCI repository were carried out. The results of experiments confirmed that proposed algorithms in general achieve significantly better results than exhaustive search method. It also helped to determine the most promising version of the algorithm we propose. Practical Relevance. The suggested algorithm can be successfully used for model selection and configuration for clustering algorithms, and can be applied in a wide range of clustering tasks in various areas, including biology, psychology, and image analysis.