Statistical versus Distance-Based Meta-Features for Clustering Algorithm recommendation Using Meta-Learning
Bruno Almeida Pimentel, André C. P. L. F. de Carvalho · 2018
When a Machine Learning algorithm is applied to a dataset, the predictive performance of the algorithm depends on how suitable its bias is to the the data distribution in the dataset, which leads researchers to create a large number of algorithms. The recommendation of the most suitable algorithm for a new dataset can occur by trial and error, trying a large number of algorithms with distinct bias. However, this approach usually has a high computational cost. This cost could be reduced if the most suitable algorithm(s) could be recommended. Meta-learning has been successfully used for recommendation of the best Machine Learning algorithm in several Machine Learning tasks. Meta-learning can rank algorithms according to their adequacy for a new dataset and use this ranking to recommend the algorithms to be used. As the recommended ranking is based on dataset features, dataset characterization (using meta-features) is of crucial importance for the successful use of meta-learning. Clustering is one of the main application of Machine Learning algorithms, however few works investigate the use of meta-learning for the recommendation of clustering algorithms. Moreover, the existing works use a poor methodology for the evaluation of the algorithm recommendation method and a small number of datasets. This paper proposes a comparison between two types of meta-features for clustering algorithm recommendation using meta-learning. Experimental results show in which situations the use of each type of meta-features is more suitable.