Paper Info
Title
Group-penalized feature selection and robust twin SVM classification via second-order cone programming.
Abstract
Selecting the relevant factors in a particular domain is of utmost interest in the machine learning community. This paper concerns the feature selection process for twin support vector machine (TWSVM), a powerful classification method that constructs two nonparallel hyperplanes in order to define a classification rule. Besides the Euclidean norm, our proposal includes a second regularizer that aims at eliminating variables in both twin hyperplanes in a synchronized fashion. The baseline classifier is a twin SVM implementation based on second-order cone programming, which confers robustness to the approach and leads to potentially better predictive performance compared to the standard TWSVM formulation. The proposal is studied empirically and compared with well-known feature selection methods using microarray datasets, on which it succeeds at finding low-dimensional solutions with highest average performance among all the other methods studied in this work. HighlightsNovel feature selection approach for twin SVM based on second-order cone programming.Extension of the Group Lasso penalty for coordinated variable selection.A single optimization problem is used to solve both twin subproblems.Superior performance is achieved in experiments on high-dimensional datasets.
Year
DOI
Venue
2017
10.1016/j.neucom.2017.01.005
Neurocomputing
Keywords
Field
DocType
Support vector machines,Feature selection,Twin SVM,Second-order cone programming,Group penalty
Second-order cone programming,Classification rule,Pattern recognition,Feature selection,Support vector machine,Euclidean distance,Robustness (computer science),Artificial intelligence,Classifier (linguistics),Optimization problem,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
235
C
0925-2312
Citations 
PageRank 
References 
5
0.42
23
Authors
2
Name
Order
Citations
PageRank
Julio López112413.49
Sebastián Maldonado250832.45