Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combined SVM-Based Feature Selection and Classification
Machine Learning
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
Effective input variable selection for function approximation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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In our previous work, we have developed methods for selecting input variables for function approximation based on block addition and block deletion. In this paper, we extend these methods to feature selection. To avoid random tie breaking for a small sample size problem with a large number of features, we introduce the weighted sum of the recognition error rate and the average of margin errors as the feature selection and feature ranking criteria. In our methods, starting from the empty set of features, we add several features at a time until a stopping condition is satisfied. Then we search deletable features by block deletion. To further speedup feature selection, we use a linear programming support vector machine (LP SVM) as a preselector. By computer experiments using benchmark data sets we show that the addition of the average of margin errors is effective for small sample size problems with large numbers of features in realizing high generalization ability.