Advances in neural information processing systems 2
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An introduction to variable and feature selection
The Journal of Machine Learning Research
Genetic Programming and Evolvable Machines
A rank sum test method for informative gene discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Significance of Gene Ranking for Classification of Microarray Samples
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene subset selection in kernel-induced feature space
Pattern Recognition Letters
A Blocking Strategy to Improve Gene Selection for Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Extracting Positive and Negative Association Classification Rules from RBF Kernel
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Monte Carlo feature selection for supervised classification
Bioinformatics
An Information Criterion for Variable Selection in Support Vector Machines
The Journal of Machine Learning Research
Class dependent feature scaling method using naive Bayes classifier for text datamining
Pattern Recognition Letters
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Feature selection algorithms to find strong genes
Pattern Recognition Letters
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Linear kernel Support Vector Machine Recursive Feature Elimination (SVM-RFE) is known as an excellent feature selection algorithm. Nonlinear SVM is a black box classifier for which we do not know the mapping function $${\Phi}$$ explicitly. Thus, the weight vector w cannot be explicitly computed. In this paper, we proposed a feature selection algorithm utilizing Support Vector Machine with RBF kernel based on Recursive Feature Elimination(SVM-RBF-RFE), which expands nonlinear RBF kernel into its Maclaurin series, and then the weight vector w is computed from the series according to the contribution made to classification hyperplane by each feature. Using $${w_i^2}$$ as ranking criterion, SVM-RBF-RFE starts with all the features, and eliminates one feature with the least squared weight at each step until all the features are ranked. We use SVM and KNN classifiers to evaluate nested subsets of features selected by SVM-RBF-RFE. Experimental results based on 3 UCI and 3 microarray datasets show SVM-RBF-RFE generally performs better than information gain and SVM-RFE.