Feature Ranking Using Linear SVM

Yin-Wen Chang, Chih-Jen Lin
; Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, PMLR 3:53-64, 2008.

Abstract

Feature ranking is useful to gain knowledge of data and identify relevant features. This article explores the performance of combining linear support vector machines with various feature ranking methods, and reports the experiments conducted when participating the Causality Challenge. Experiments show that a feature ranking using weights from linear SVM models yields good performances, even when the training and testing data are not identically distributed. Checking the difference of Area Under Curve (AUC) with and without removing each feature also gives similar rankings. Our study indicates that linear SVMs with simple feature rankings are effective on data sets in the Causality Challenge.

Cite this Paper


BibTeX
@InProceedings{pmlr-v3-chang08a, title = {Feature Ranking Using Linear SVM}, author = {Yin-Wen Chang and Chih-Jen Lin}, booktitle = {Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008}, pages = {53--64}, year = {2008}, editor = {Isabelle Guyon and Constantin Aliferis and Greg Cooper and André Elisseeff and Jean-Philippe Pellet and Peter Spirtes and Alexander Statnikov}, volume = {3}, series = {Proceedings of Machine Learning Research}, address = {Hong Kong}, month = {03--04 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v3/chang08a/chang08a.pdf}, url = {http://proceedings.mlr.press/v3/chang08a.html}, abstract = {Feature ranking is useful to gain knowledge of data and identify relevant features. This article explores the performance of combining linear support vector machines with various feature ranking methods, and reports the experiments conducted when participating the Causality Challenge. Experiments show that a feature ranking using weights from linear SVM models yields good performances, even when the training and testing data are not identically distributed. Checking the difference of Area Under Curve (AUC) with and without removing each feature also gives similar rankings. Our study indicates that linear SVMs with simple feature rankings are effective on data sets in the Causality Challenge.} }
Endnote
%0 Conference Paper %T Feature Ranking Using Linear SVM %A Yin-Wen Chang %A Chih-Jen Lin %B Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 %C Proceedings of Machine Learning Research %D 2008 %E Isabelle Guyon %E Constantin Aliferis %E Greg Cooper %E André Elisseeff %E Jean-Philippe Pellet %E Peter Spirtes %E Alexander Statnikov %F pmlr-v3-chang08a %I PMLR %J Proceedings of Machine Learning Research %P 53--64 %U http://proceedings.mlr.press %V 3 %W PMLR %X Feature ranking is useful to gain knowledge of data and identify relevant features. This article explores the performance of combining linear support vector machines with various feature ranking methods, and reports the experiments conducted when participating the Causality Challenge. Experiments show that a feature ranking using weights from linear SVM models yields good performances, even when the training and testing data are not identically distributed. Checking the difference of Area Under Curve (AUC) with and without removing each feature also gives similar rankings. Our study indicates that linear SVMs with simple feature rankings are effective on data sets in the Causality Challenge.
RIS
TY - CPAPER TI - Feature Ranking Using Linear SVM AU - Yin-Wen Chang AU - Chih-Jen Lin BT - Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 PY - 2008/12/31 DA - 2008/12/31 ED - Isabelle Guyon ED - Constantin Aliferis ED - Greg Cooper ED - André Elisseeff ED - Jean-Philippe Pellet ED - Peter Spirtes ED - Alexander Statnikov ID - pmlr-v3-chang08a PB - PMLR SP - 53 DP - PMLR EP - 64 L1 - http://proceedings.mlr.press/v3/chang08a/chang08a.pdf UR - http://proceedings.mlr.press/v3/chang08a.html AB - Feature ranking is useful to gain knowledge of data and identify relevant features. This article explores the performance of combining linear support vector machines with various feature ranking methods, and reports the experiments conducted when participating the Causality Challenge. Experiments show that a feature ranking using weights from linear SVM models yields good performances, even when the training and testing data are not identically distributed. Checking the difference of Area Under Curve (AUC) with and without removing each feature also gives similar rankings. Our study indicates that linear SVMs with simple feature rankings are effective on data sets in the Causality Challenge. ER -
APA
Chang, Y. & Lin, C.. (2008). Feature Ranking Using Linear SVM. Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008, in PMLR 3:53-64

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