Abstract
The \( k \)-Nearest Neighbor (k-NN) classifier has been applied to the identification of cancer samples using the gene expression profiles with encouraging results. However, the performance of \( k \)-NN depends strongly on the distance considered to evaluate the sample proximities. Besides, the choice of a good dissimilarity is a difficult task and depends on the problem at hand. In this chapter, we introduce a method to learn the metric from the data to improve the \( k \)-NN classifier. To this aim, we consider a regularized version of the kernel alignment algorithm that incorporates a term that penalizes the complexity of the family of distances avoiding overfitting. The error function is optimized using a semidefinite programming approach (SDP). The method proposed has been applied to the challenging problem of cancer identification using the gene expression profiles. Kernel alignment \( k \)-NN outperforms other metric learning strategies and improves the classical \( k \)-NN algorithm.
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Martín-Merino, M. (2010). k-NN for the Classification of Human Cancer Samples Using the Gene Expression Profiles. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_18
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_18
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