Abstract
Gene ranking is widely employed in gene selection. Most criteria adopt a single quantitative value to rank genes. To some degree, they hardly provide comprehensive discriminative information of genes. In this paper, the supervised vector representation is proposed. The supervised vector reflecting the spatial distribution in the space expended by the gene is used as the gene representation. The possible problems of “bias” and “cumulative error”, which may be induced by the criteria based on a single value, can be avoided by the proposed criterion. An algorithm of gene selection based on supervised vector representation is also proposed to select gene subsets with the new criterion and its performance is compared with that of some existing gene selection algorithms. Experimental results demonstrate that the proposed algorithm is capable of generating the final gene subsets with higher predictive capability in most cases.
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Yu, T., Gao, F., Jin, H., Wei, J. (2014). Gene Selection Based on Supervised Vector Representation of Genes. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_67
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DOI: https://doi.org/10.1007/978-3-319-13560-1_67
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
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