Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00521-003-0362-3
Title: Saliency analysis of support vector machines for gene selection in tissue classification
Authors: Cao, L.
Seng, C.K. 
Gu, Q.
Lee, H.P. 
Keywords: Feature selection
Saliency analysis
Support vector machines
Issue Date: May-2003
Citation: Cao, L., Seng, C.K., Gu, Q., Lee, H.P. (2003-05). Saliency analysis of support vector machines for gene selection in tissue classification. Neural Computing and Applications 11 (3-4) : 244-249. ScholarBank@NUS Repository. https://doi.org/10.1007/s00521-003-0362-3
Abstract: This paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for gene selection in tissue classification. The importance of genes is ranked by evaluating the sensitivity of the output to the inputs in terms of the partial derivative. A systematic learning algorithm called the Recursive Saliency Analysis (RSA) algorithm is developed to remove irrelevant genes. One simulated data and two gene expression data sets for tissue classification are evaluated in the experiment. The simulation results demonstrate that RSA is effective in SVMs for identifying important genes.
Source Title: Neural Computing and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/130447
ISSN: 09410643
DOI: 10.1007/s00521-003-0362-3
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