Abstract
SAGE data can be used to learn classification models to aid cancer classification. In this paper, maximum entropy models are built for SAGE data classification by estimating the conditional distribution of the class variable given the samples. In experiments we compare accuracy and precision to SVMs (one of the most effective classifiers in performing accurate cancer diagnosis from microarray gene expression data) and show that maximum entropy is better. The results indicate that maximum entropy is a promising technique for SAGE data classification.
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Xin, J., Bie, R. (2005). Classification Analysis of SAGE Data Using Maximum Entropy Model. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_133
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DOI: https://doi.org/10.1007/11540007_133
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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