Experimental investigation of classification algorithms for ITS dataset
J.L. Yearwood, Byeong Ho Kang, AV Kelarev · eCite Digital Repository (University of Tasmania) · 2008
This article is devoted to experimental investigation of classification algorithms for analysis of ITS dataset. We introduce and consider a novel k-committees algorithm for classification and compare it with the discrete k-means and Nearest Neighbour algorithms. The ITS dataset consists of nuclear ribosomal DNA sequences, where rather sophisticated alignment scores have to be used as a measure of distance. These scores do not form a Minkowski metric and the sequences cannot be regarded as points in a finite dimensional space. This is why it is necessary to develop novel algorithms and adjust familiar ones. We present the results of experiments comparing the efficiency of three classification methods in their ability to achieve agreement with classes published in the biological literature before. It turns out that our algorithms are efficient and can be used to obtain biologically significant classifications. A simplified version of a synthetic dataset, where the k-committees classifier outperforms k-means and Nearest Neighbour classifiers, is also presented.