Hybrid classification of high-dimensional biomedical tumour datasets

Abstract

This paper concerns hybrid approach to classification of high-dimensional tumour data. The research presents a comparison of hybrid classification methods: bagging with Naive Bayes (NaiveBayes), IBk, J48 and SMO as base classifiers, random forest as a variant of bagging with a decision tree as a base classifier, boosting with NaiveBayes, SMO, IBk and J48 as base classifiers, and voting by all single classifiers using majority as a combination rule, as well as five single classification strategies, including k-nearest neighbours (IBk), J48, NaiveBayes, random tree and sequential minimal optimization algorithm for training support vector machines. The major conclusion drawn from the study was that hybrid classifiers has demonstrated its potential ability to accurately and efficiently classify both binary and multiclass high-dimensional sets of tumour specimens.