Abstract
In this paper, we propose a gene extraction method by using two standard feature extraction methods, namely the T-test method and kernel partial least squares (KPLS), in tandem. First, a preprocessing step based on the T-test method is used to filter irrelevant and noisy genes. KPLS is then used to extract features with high information content. Finally, the extracted features are fed into a classifier. Experiments are performed on three benchmark datasets: breast cancer, ALL/AML leukemia and colon cancer. While using either the T-test method or KPLS does not yield satisfactory results, experimental results demonstrate that using these two together can significantly boost classification accuracy, and this simple combination can obtain state-of-the-art performance on all three datasets.
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, S., Liao, C., Kwok, J.T. (2006). Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_2
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DOI: https://doi.org/10.1007/11893295_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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