Classification of Microarrays with kNN: Comparison of Dimensionality Reduction Methods
2007 (English)In: Intelligent Data Engineering and Automated Learning - IDEAL 2007 / [ed] Hujun Yin, Peter Tino, Emilio Corchado, Will Byrne, Xin Yao, Berlin, Heidelberg: Springer Verlag , 2007, 800-809 p.Conference paper (Refereed)
Dimensionality reduction can often improve the performance of the k-nearest neighbor classifier (kNN) for high-dimensional data sets, such as microarrays. The effect of the choice of dimensionality reduction method on the predictive performance of kNN for classifying microarray data is an open issue, and four common dimensionality reduction methods, Principal Component Analysis (PCA), Random Projection (RP), Partial Least Squares (PLS) and Information Gain(IG), are compared on eight microarray data sets. It is observed that all dimensionality reduction methods result in more accurate classifiers than what is obtained from using the raw attributes. Furthermore, it is observed that both PCA and PLS reach their best accuracies with fewer components than the other two methods, and that RP needs far more components than the others to outperform kNN on the non-reduced dataset. None of the dimensionality reduction methods can be concluded to generally outperform the others, although PLS is shown to be superior on all four binary classification tasks, but the main conclusion from the study is that the choice of dimensionality reduction method can be of major importance when classifying microarrays using kNN.
Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer Verlag , 2007. 800-809 p.
, Lecture Notes in Computer Science, 4881/2007
IdentifiersURN: urn:nbn:se:su:diva-37828DOI: 10.1007/978-3-540-77226-2_80ISBN: 978-3-540-77225-5OAI: oai:DiVA.org:su-37828DiVA: diva2:305374
8th International Conference on Intelligent Data Engineering and Automated Learning, LNCS 4881