A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases

Juanying Xie, Weixin Xie, Chunxia Wang, Xinbo Gao
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:142-151, 2010.

Abstract

This paper developed a diagnosis model based on Support Vector Machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our hybrid feature selection method, named IFSFFS (Improved F-score and Sequential Forward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In our IFSFFS, we firstly generalized the original F-score to the improved F-score measuring the discrimination of more than two sets of real numbers. Then we proposed to combine Sequential Forward Floating Search (SFFS) and our improved F-score to accomplish the optimal feature subset selection. Where, our improved F-score is an evaluation criterion for filters, while SFFS and SVM compose an evaluation system of wrappers. The best parameters of kernel function of SVM are found out by grid search technique with ten-fold cross validation. Experiments have been conducted on five random training-test partitions of the erythemato-squamous diseases dataset from UCI machine learning database. The experimental results show that our SVM-based model with IFSFFS achieved the optimal classification accuracy with no more than 14 features as well.

Cite this Paper

BibTeX
@InProceedings{pmlr-v11-xie10a, title = {A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases}, author = {Xie, Juanying and Xie, Weixin and Wang, Chunxia and Gao, Xinbo}, booktitle = {Proceedings of the First Workshop on Applications of Pattern Analysis}, pages = {142--151}, year = {2010}, editor = {Diethe, Tom and Cristianini, Nello and Shawe-Taylor, John}, volume = {11}, series = {Proceedings of Machine Learning Research}, address = {Cumberland Lodge, Windsor, UK}, month = {01--03 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v11/xie10a/xie10a.pdf}, url = {https://proceedings.mlr.press/v11/xie10a.html}, abstract = {This paper developed a diagnosis model based on Support Vector Machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our hybrid feature selection method, named IFSFFS (Improved F-score and Sequential Forward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In our IFSFFS, we firstly generalized the original F-score to the improved F-score measuring the discrimination of more than two sets of real numbers. Then we proposed to combine Sequential Forward Floating Search (SFFS) and our improved F-score to accomplish the optimal feature subset selection. Where, our improved F-score is an evaluation criterion for filters, while SFFS and SVM compose an evaluation system of wrappers. The best parameters of kernel function of SVM are found out by grid search technique with ten-fold cross validation. Experiments have been conducted on five random training-test partitions of the erythemato-squamous diseases dataset from UCI machine learning database. The experimental results show that our SVM-based model with IFSFFS achieved the optimal classification accuracy with no more than 14 features as well.} }
Endnote
%0 Conference Paper %T A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases %A Juanying Xie %A Weixin Xie %A Chunxia Wang %A Xinbo Gao %B Proceedings of the First Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2010 %E Tom Diethe %E Nello Cristianini %E John Shawe-Taylor %F pmlr-v11-xie10a %I PMLR %P 142--151 %U https://proceedings.mlr.press/v11/xie10a.html %V 11 %X This paper developed a diagnosis model based on Support Vector Machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our hybrid feature selection method, named IFSFFS (Improved F-score and Sequential Forward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In our IFSFFS, we firstly generalized the original F-score to the improved F-score measuring the discrimination of more than two sets of real numbers. Then we proposed to combine Sequential Forward Floating Search (SFFS) and our improved F-score to accomplish the optimal feature subset selection. Where, our improved F-score is an evaluation criterion for filters, while SFFS and SVM compose an evaluation system of wrappers. The best parameters of kernel function of SVM are found out by grid search technique with ten-fold cross validation. Experiments have been conducted on five random training-test partitions of the erythemato-squamous diseases dataset from UCI machine learning database. The experimental results show that our SVM-based model with IFSFFS achieved the optimal classification accuracy with no more than 14 features as well.
RIS
TY - CPAPER TI - A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases AU - Juanying Xie AU - Weixin Xie AU - Chunxia Wang AU - Xinbo Gao BT - Proceedings of the First Workshop on Applications of Pattern Analysis DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-xie10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 11 SP - 142 EP - 151 L1 - http://proceedings.mlr.press/v11/xie10a/xie10a.pdf UR - https://proceedings.mlr.press/v11/xie10a.html AB - This paper developed a diagnosis model based on Support Vector Machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our hybrid feature selection method, named IFSFFS (Improved F-score and Sequential Forward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In our IFSFFS, we firstly generalized the original F-score to the improved F-score measuring the discrimination of more than two sets of real numbers. Then we proposed to combine Sequential Forward Floating Search (SFFS) and our improved F-score to accomplish the optimal feature subset selection. Where, our improved F-score is an evaluation criterion for filters, while SFFS and SVM compose an evaluation system of wrappers. The best parameters of kernel function of SVM are found out by grid search technique with ten-fold cross validation. Experiments have been conducted on five random training-test partitions of the erythemato-squamous diseases dataset from UCI machine learning database. The experimental results show that our SVM-based model with IFSFFS achieved the optimal classification accuracy with no more than 14 features as well. ER -
APA
Xie, J., Xie, W., Wang, C. & Gao, X.. (2010). A Novel Hybrid Feature Selection Method Based on IFSFFS and SVM for the Diagnosis of Erythemato-Squamous Diseases. Proceedings of the First Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 11:142-151 Available from https://proceedings.mlr.press/v11/xie10a.html.

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