PreCLAS: An Evolutionary Tool for Unsupervised Feature Selection

CARBALLIDO, JESSICA A.; PONZONI, IGNACIO; CECCHINI, ROCÍO L.

Gijón

Conferencia; 15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020; 2020

Computer Sciences Department, University of Oviedo

Several research areas are being faced with data matrices that are not suitable to be managed with traditional clustering, regression, or classification strategies. For example, biological so-called omicproblems present models with thousands or millions of rows and less than a hundred columns. This matrix structure hinders the successful progress of traditional data analysis methods and thus needs some means for reducing the number of rows. This article presents an unsupervisedapproach called PreCLAS for preprocessing matrices with dimension problems to obtain data that are apt for clustering and classification strategies. The PreCLAS was implemented as an unsupervised strategy that aims at finding a submatrix with a drastically reduced number ofrows, preferring those rows that together present some group structure. Experimentation was carried out in two stages. First, to assess its functionality, a benchmark dataset was studied in a clustering context. Then, a microarray dataset with genomic information was analyzed, and thePreCLAS was used to select informative genes in the context of classification strategies. Experimentation showed that the new method performs successfully at drastically reducing the number of rows of a matrix, smartly performing unsupervised feature selection for both classification and clustering problems.