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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 7347986, 7 pages
Research Article

Feature Scaling via Second-Order Cone Programming

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China

Received 20 January 2016; Accepted 3 April 2016

Academic Editor: Julien Bruchon

Copyright © 2016 Zhizheng Liang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Feature scaling has attracted considerable attention during the past several decades because of its important role in feature selection. In this paper, a novel algorithm for learning scaling factors of features is proposed. It first assigns a nonnegative scaling factor to each feature of data and then adopts a generalized performance measure to learn the optimal scaling factors. It is of interest to note that the proposed model can be transformed into a convex optimization problem: second-order cone programming (SOCP). Thus the scaling factors of features in our method are globally optimal in some sense. Several experiments on simulated data, UCI data sets, and the gene data set are conducted to demonstrate that the proposed method is more effective than previous methods.