k-Top Scoring Pair Algorithm for feature selection in SVM with applications to microarray data classification

• Published in: Soft computing (Berlin, Germany) Vol. 14; no. 2; pp. 151 - 159
• Main Authors: Yoon, Sejong, Kim, Saejoon
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.01.2010; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Classifiers; Computational Intelligence; Control; Datasets; Engineering; Feature selection; Focus; Gene expression; Machine learning; Mathematical Logic and Foundations; Mechatronics; Neural networks; Public domain; Robotics; Support vector machines; Traveling salesman problem; TSP; SVM; Ensemble methods; SVM-RFE; Top Scoring Pair
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-009-0437-x

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.