Feature Selection and Classification Pairwise Combinations for High dimensional Tumour Biomedical Datasets
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"ive 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.