Abstract
Top Scoring Pair (TSP) and its ensemble counterpart, k-Top Scoring Pair (k-TSP), were recently introduced as competitive options for solving classification problems of microarray data. However, support vector machine (SVM) which was compared with these approaches is not equipped with feature or variable selection mechanism while TSP itself is a kind of variable selection algorithm. Moreover, an ensemble of SVMs should also be considered as a possible competitor to k-TSP. In this work, we conducted a fair comparison between TSP and SVM-recursive feature elimination (SVM-RFE) as the feature selection method for SVM. We also compared k-TSP with two ensemble methods using SVM as their base classifier. Results on ten public domain microarray data indicated that TSP family classifiers serve as good feature selection schemes which may be combined effectively with other classification methods.
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Notes
http://faculty.vassar.edu/lowry/kappa.htm. Accessed 11 Mar 2009
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Acknowledgements
The authors would like to appreciate anonymous reviewers for their valuable comments that improved the presentation of this paper. The work of S. Kim was supported by the Special Research Grant of Sogang University 200811028.01.
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Yoon, S., Kim, S. k-Top Scoring Pair Algorithm for feature selection in SVM with applications to microarray data classification. Soft Comput 14, 151–159 (2010). https://doi.org/10.1007/s00500-009-0437-x
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DOI: https://doi.org/10.1007/s00500-009-0437-x