Stable feature selection with support vector machines Kamkar, Iman, Gupta, Sunil K., Phung, Dinh and Venkatesh, Svetha 2015, Stable feature selection with support vector machines. In Pfahringer, Bernhard and Renz, Jochen (ed), AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 Proceedings, Springer, Berlin, Germany, pp.298-308, doi: 10.1007/978-3-319-26350-2_26. Attached Files Name Description MIMEType Size Downloads Title Stable feature selection with support vector machines Author(s) Kamkar, Iman Gupta, Sunil K. orcid.org/0000-0002-3308-1930 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Title of book AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 Proceedings Editor(s) Pfahringer, Bernhard Renz, Jochen Publication date 2015 Series Lecture notes in computer science; v.9457 Chapter number 25 Total chapters 57 Start page 298 End page 308 Total pages 11 Publisher Springer Place of Publication Berlin, Germany Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Robotics Computer Science REGRESSION SHRINKAGE VARIABLE SELECTION MODEL SELECTION BREAST-CANCER LASSO PREDICTOR SIGNATURE MORTALITY SURVIVAL Summary The support vector machine (SVM) is a popular method for classification, well known for finding the maximum-margin hyperplane. Combining SVM with l1-norm penalty further enables it to simultaneously perform feature selection and margin maximization within a single framework. However, l1-norm SVM shows instability in selecting features in presence of correlated features. We propose a new method to increase the stability of l1-norm SVM by encouraging similarities between feature weights based on feature correlations, which is captured via a feature covariance matrix. Our proposed method can capture both positive and negative correlations between features. We formulate the model as a convex optimization problem and propose a solution based on alternating minimization. Using both synthetic and real-world datasets, we show that our model achieves better stability and classification accuracy compared to several state-of-the-art regularized classification methods. ISBN 9783319263502 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-26350-2_26 Field of Research 080109 Pattern Recognition and Data Mining 08 Information And Computing Sciences Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1 Book chapter ERA Research output type B Book chapter Copyright notice ©2015, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30081381 Document type: Book Chapter Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 15 Feb 2016, 16:00:16 EST Mon, 15 Feb 2016, 16:01:23 EST Tue, 16 Feb 2016, 04:01:24 EST Tue, 16 Feb 2016, 05:09:57 EST Tue, 12 Apr 2016, 09:52:04 EST Tue, 12 Apr 2016, 09:54:21 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 269 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 15 Feb 2016, 16:01:23 EST

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