%0 Journal Article
%T Feature Selection and Classification Pairwise Combinations for High-dimensional Tumour Biomedical Datasets
%A Wosiak, Agnieszka
%A Dziomdziora, Agata
%J Schedae Informaticae
%V 2015
%R 10.4467/20838476SI.15.005.3027
%N Volume 24
%P 53-62
%K dictionary learning, sparse representation, anomaly detection
%@ 1732-3916
%D 2016
%U https://ejournals.eu/en/journal/schedae-informaticae/article/feature-selection-and-classification-pairwise-combinations-for-high-dimensional-tumour-biomedical-datasets
%X 
	This paper concerns classification of high-dimensional yet small sample size biomedical data and feature selection aimed at reducing dimensionality of the microarray data. The research presents a comparison of pairwise combinations of six classification strategies, including decision trees, logistic model trees, Bayes network, Na¨ıve Bayes, k-nearest neighbours and sequential minimal optimization algorithm for training support vector machines, as well as seven attribute selection methods: Correlation-based Feature Selection, chi-squared, information gain, gain ratio, symmetrical uncertainty, ReliefF and SVM-RFE (Support Vector Machine-Recursive Feature Elimination). In this paper, SVMRFE feature selection technique combined with SMO classifier has demonstrated its potential ability to accurately and efficiently classify both binary and multiclass high-dimensional sets of tumour specimens.