Multi-class feature selection for texture classification
Pattern Recognition Letters
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Extracting gene regulation information for cancer classification
Pattern Recognition
Artificial Intelligence in Medicine
Constructing the gene regulation-level representation of microarray data for cancer classification
Journal of Biomedical Informatics
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Discrimination of myocardial infarction stages by subjective feature extraction
Computer Methods and Programs in Biomedicine
Journal of Biomedical Informatics
Feature subset selection in large dimensionality domains
Pattern Recognition
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Expert Systems with Applications: An International Journal
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Content-based image retrieval by combining genetic algorithm and support vector machine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Feature selection for SVM via optimization of kernel polarization with Gaussian ARD kernels
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Combining functional networks and sensitivity analysis as wrapper method for feature selection
Expert Systems with Applications: An International Journal
A method for feature selection on microarray data using support vector machine
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Expert Systems with Applications: An International Journal
A minority class feature selection method
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets
Information Sciences: an International Journal
A novel divide-and-merge classification for high dimensional datasets
Computational Biology and Chemistry
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In many pattern classification applications, data are represented by high dimensional feature vectors, which induce high computational cost and reduce classification speed in the context of support vector machines (SVMs). To reduce the dimensionality of pattern representation, we develop a discriminative function pruning analysis (DFPA) feature subset selection method in the present study. The basic idea of the DFPA method is to learn the SVM discriminative function from training data using all input variables available first, and then to select feature subset through pruning analysis. In the present study, the pruning is implement using a forward selection procedure combined with a linear least square estimation algorithm, taking advantage of linear-in-the-parameter structure of the SVM discriminative function. The strength of the DFPA method is that it combines good characters of both filter and wrapper methods. Firstly, it retains the simplicity of the filter method avoiding training of a large number of SVM classifier. Secondly, it inherits the good performance of the wrapper method by taking the SVM classification algorithm into account.