Features Selection Algorithms for Classification of Voice Signals

Silva, Letícia; Bispo, Bruno; Teixeira, João Paulo

http://hdl.handle.net/10198/24673

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Autores

Silva, Letícia

Bispo, Bruno

Teixeira, João Paulo

Resumo(s)

In data mining problems, the high dimensionality of the input features can affect the performance of the process. In this way, the features selection methods appear as a solution to the problems encountered when analyzing databases with large dimensions. This article presents the implementation of the Pearson's linear correlation, ReliefF, Welch's t-test and multilinear regression based algorithms with forwards selection and backward elimination direction for the selection of acoustic features for the task of voice pathologies identification. The best set of selected features improved the accuracy and F1-score from 83% to 92% (9 points of percentage), using the ReliefF algorithm.

Palavras-chave

Backward elimination Forward selection Multilinear regression analysis Pearson correlation ReliefF Welch's t-test

URI

http://hdl.handle.net/10198/24673

Citação

Silva, Letícia; Bispo, Bruno; Teixeira, João Paulo (2021). Features selection algorithms for classification of voice signals. In International Conference on ENTERprise Information Systems (CENTERIS), International Conference on Project MANagement (ProjMAN), International Conference on Health and Social Care Information Systems and Technologies (HCist). Procedia Computer Science. ISSN 1877-0509. p. 948-956.