Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23067
Title: Feature selection for pairwise scoring kernels with applications to protein subcellular localization
Authors: Kung, SY
Mak, MW 
Keywords: Feature selection
SVM
Kernel methods
Pairwise scoring
Subcellular localization
Issue Date: 2007
Publisher: IEEE
Source: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007 : ICASSP 2007, 15-20 April 2007, Honolulu, HI, p. II569-II572 How to cite?
Abstract: In biological sequence classification, it is common to convert variable-length sequences into fixed-length vectors via pairwise sequence comparison. This pairwise approach, however, can lead to feature vectors with dimension equal to the training set size, causing the curse of dimensionality. This calls for feature selection methods that can weed out irrelevant features to reduce training and recognition time. In this paper, we propose to train an SVM using the full-feature column vectors of a pairwise scoring matrix and select the relevant features based on the support vectors of the SVM. The idea stems from the fact that pairwise scoring matrices are symmetric and support vectors are important for classification. We refer to this approach as vector-index-adaptive SVM (VIA-SVM). We compare VIA-SVM with other feature selection schemes-including SVM-RFE, R-SVM, and a filter method based on symmetric divergence (SD)-in protein subcellular localization. Results show that VIA-SVM is able to automatically bound the number of selected features within a small range. We also found that fusion of VIA-SVM and SD can produce more compact feature subsets without decreasing prediction accuracy, and that while VIA-SVM is superior for large feature-set size, the combination of SD and VIA-SVM performs better at small feature-set size.
URI: http://hdl.handle.net/10397/23067
ISBN: 1-4244-0727-3
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2007.366299
Appears in Collections:Conference Paper

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