An improved 1-norm SVM for simultaneous classification and variable selection

Hui Zou

An improved 1-norm SVM for simultaneous classification and variable selection

Hui Zou

Statistics (Twin Cities)

Research output: Contribution to journal › Conference article › peer-review

40 Scopus citations

Abstract

We propose a novel extension of the 1-norm support vector machine (SVM) for simultaneous feature selection and classification. The new algorithm penalizes the empirical hinge loss by the adaptively weighted 1-norm penalty in which the weights are computed by the 2-norm SVM. Hence the new algorithm is called the hybrid SVM. Simulation and real data examples show that the hybrid SVM not only often improves upon the 1-norm SVM in terms of classification accuracy but also enjoys better feature selection performance.

Original language	English (US)
Pages (from-to)	675-681
Number of pages	7
Journal	Journal of Machine Learning Research
Volume	2
State	Published - 2007
Event	11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007 - San Juan, Puerto Rico Duration: Mar 21 2007 → Mar 24 2007

Find It

Find It at U of M Libraries

Cite this

@article{06e647ea00b14f73b61f94f621c747d9,

title = "An improved 1-norm SVM for simultaneous classification and variable selection",

abstract = "We propose a novel extension of the 1-norm support vector machine (SVM) for simultaneous feature selection and classification. The new algorithm penalizes the empirical hinge loss by the adaptively weighted 1-norm penalty in which the weights are computed by the 2-norm SVM. Hence the new algorithm is called the hybrid SVM. Simulation and real data examples show that the hybrid SVM not only often improves upon the 1-norm SVM in terms of classification accuracy but also enjoys better feature selection performance.",

author = "Hui Zou",

year = "2007",

language = "English (US)",

volume = "2",

pages = "675--681",

journal = "Journal of Machine Learning Research",

issn = "1532-4435",

publisher = "Microtome Publishing",

note = "11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007 ; Conference date: 21-03-2007 Through 24-03-2007",

}

TY - JOUR

T1 - An improved 1-norm SVM for simultaneous classification and variable selection

AU - Zou, Hui

PY - 2007

Y1 - 2007

N2 - We propose a novel extension of the 1-norm support vector machine (SVM) for simultaneous feature selection and classification. The new algorithm penalizes the empirical hinge loss by the adaptively weighted 1-norm penalty in which the weights are computed by the 2-norm SVM. Hence the new algorithm is called the hybrid SVM. Simulation and real data examples show that the hybrid SVM not only often improves upon the 1-norm SVM in terms of classification accuracy but also enjoys better feature selection performance.

AB - We propose a novel extension of the 1-norm support vector machine (SVM) for simultaneous feature selection and classification. The new algorithm penalizes the empirical hinge loss by the adaptively weighted 1-norm penalty in which the weights are computed by the 2-norm SVM. Hence the new algorithm is called the hybrid SVM. Simulation and real data examples show that the hybrid SVM not only often improves upon the 1-norm SVM in terms of classification accuracy but also enjoys better feature selection performance.

UR - https://www.scopus.com/pages/publications/79960299381

UR - https://www.scopus.com/pages/publications/79960299381#tab=citedBy

M3 - Conference article

AN - SCOPUS:79960299381

SN - 1532-4435

VL - 2

SP - 675

EP - 681

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

T2 - 11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

Y2 - 21 March 2007 through 24 March 2007

ER -

An improved 1-norm SVM for simultaneous classification and variable selection

Abstract

Find It

Other files and links

Fingerprint

Cite this