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Authors: Qingzhong Liu 1 ; Andrew H. Sung 2 and Bernardete M. Ribeiro 3

Affiliations: 1 New Mexico Tech, United States ; 2 New Mexico Tech, Socorro; Institute for Complex Additive Systems Analysis, New Mexico Tech, Socorro, United States ; 3 University of Coimbra, Portugal

Abstract: Class prediction and feature selection are two learning tasks that are strictly paired in the search of molecular profiles from microarray data. In this paper, we apply the recursive gene selection proposed in our previous paper to six types of micaroarray gene expression data for tumor classification. In comparison with other two well-known gene selections, SVM-RFE (Support Vector Machine Recursive Feature Elimination) and T-test, our method outperforms best. The kernel type and kernel parameters are critical to the classification performances for the kernel classifiers. Our experiments indicate that RBF kernel classifiers are pretty good under low feature dimensions; their performances increase initially and then decrease as the feature dimension increases.

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Paper citation in several formats:
Liu, Q.; H. Sung, A. and M. Ribeiro, B. (2006). Comparison of Gene Selection and Machine Learning for Tumor Classification. In Proceedings of the 2nd International Workshop on Biosignal Processing and Classification (ICINCO 2006) - BPC; ISBN 978-972-8865-67-2, SciTePress, pages 13-22. DOI: 10.5220/0001219700130022

@conference{bpc06,
author={Qingzhong Liu. and Andrew {H. Sung}. and Bernardete {M. Ribeiro}.},
title={Comparison of Gene Selection and Machine Learning for Tumor Classification},
booktitle={Proceedings of the 2nd International Workshop on Biosignal Processing and Classification (ICINCO 2006) - BPC},
year={2006},
pages={13-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001219700130022},
isbn={978-972-8865-67-2},
}

TY - CONF

JO - Proceedings of the 2nd International Workshop on Biosignal Processing and Classification (ICINCO 2006) - BPC
TI - Comparison of Gene Selection and Machine Learning for Tumor Classification
SN - 978-972-8865-67-2
AU - Liu, Q.
AU - H. Sung, A.
AU - M. Ribeiro, B.
PY - 2006
SP - 13
EP - 22
DO - 10.5220/0001219700130022
PB - SciTePress

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