The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
Discriminative metric design for robust pattern recognition
IEEE Transactions on Signal Processing
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Fast orthogonal forward selection algorithm for feature subset selection
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Predictor output sensitivity and feature similarity-based feature selection
Fuzzy Sets and Systems
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
Kernel discriminant analysis based feature selection
Neurocomputing
Gene Selection for Cancer Classification Using DCA
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Identifying core sets of discriminatory features using particle swarm optimization
Expert Systems with Applications: An International Journal
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Expert Systems with Applications: An International Journal
A General Framework of Feature Selection for Text Categorization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
An improved version of the wrapper feature selection method based on functional decomposition
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
The effect of linguistic hedges on feature selection: Part 2
Expert Systems with Applications: An International Journal
Feature selection for SVM via optimization of kernel polarization with Gaussian ARD kernels
Expert Systems with Applications: An International Journal
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: 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 filter based feature selection approach using lempel ziv complexity
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Information Sciences: an International Journal
Feature selection based on kernel discriminant analysis
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Evaluation of feature selection by multiclass kernel discriminant analysis
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Functional networks and analysis of variance for feature selection
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets
Information Sciences: an International Journal
Feature selection for classification of oscillating time series
Expert Systems: The Journal of Knowledge Engineering
Feature selection by block addition and block deletion
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of ''overfitting''. Feature Selection addresses the dimensionality reduction problem by determining a subset of available features which is most essential for classification. This paper presents a novel feature selection method named filtered and supported sequential forward search (FS_SFS) in the context of support vector machines (SVM). In comparison with conventional wrapper methods that employ the SFS strategy, FS_SFS has two important properties to reduce the time of computation. First, it dynamically maintains a subset of samples for the training of SVM. Because not all the available samples participate in the training process, the computational cost to obtain a single SVM classifier is decreased. Secondly, a new criterion, which takes into consideration both the discriminant ability of individual features and the correlation between them, is proposed to effectively filter out nonessential features. As a result, the total number of training is significantly reduced and the overfitting problem is alleviated. The proposed approach is tested on both synthetic and real data to demonstrate its effectiveness and efficiency. .