Título: Fuzzy C-Means com Método Wrapper Com Baixo Custo Computacional de Seleção de Atributos
Autores: Gabriel Marcondes dos Santos, Emmanuel Tavares Ferreira Affonso, Alisson Marques Da Silva and Gray Farias Moita.
Resumo:
Nowadays the Computational Intelligence (IC) algorithms have shown a lot of efficiency in pattern classification and recognition processes. However, some databases may contain irrelevant attributes that may be detrimental to the learning of the classification model. In order to detect and exclude input attributes with little representativeness in the data sets presented to the classification algorithms, the Features Selection (FS) methods are commonly used. The goal of features selection methods is to minimize the number of input attributes processed by a classifier in order to improve its assertiveness. In this way, this work aims to analyze solutions to classification problems with three different classification algorithms. The first approach used for classification is the unsupervised Fuzzy C-Means (FCM) algorithm, the second approach is a supervised version of FCM and the third approach is a variation of supervised FCM with features selection. The method of features selection incorporated in FCM is called the Mean Ratio Feature Selection (MRFS), and was developed with the objective of being a method with low computational cost, without need for complex mathematical equations and can be easily incorporated into any classifier. For the experiments, the three versions of the unsupervised FCM, supervised FCM and FCM with attribute selection were performed with the aim of verifying whether there would be a significant improvement between the variations of the FCM. The results of the experiments showed that FCM with MRFS is promising, with results superior to the original algorithm and also to its supervised version.
Palavras-chave:
Features Selection, Computational Intelligence, FCM, Wrapper Method, Low Cost Computational.
Páginas: 7
Código DOI: 10.21528/CBIC2021-87
Artigo em pdf: CBIC_2021_paper_87.pdf
Arquivo BibTeX: CBIC_2021_87.bib