Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Combining feature subsets in feature selection
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
One Lead ECG Based Personal Identification with Feature Subspace Ensembles
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Research of multi-population agent genetic algorithm for feature selection
Expert Systems with Applications: An International Journal
Two coding based adaptive parallel co-genetic algorithm with double agents structure
Engineering Applications of Artificial Intelligence
Taking advantage of class-specific feature selection
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
General framework for class-specific feature selection
Expert Systems with Applications: An International Journal
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In feature selection (FS), different strategies usually lead to different results. Even the same strategy may do so in distinct feature selection contexts.We propose a feature subspace ensemble method, consisting on the parallel combination of decisions from multiple classifiers. Each classifier is designed using variations of the feature representation space, obtained by means of FS. With the proposed approach, relevant discriminative information contained in features neglected in a single run of a FS method, may be recovered by the application of multiple FS runs or algorithms, and contribute to the decision through the classifier combination process. Experimental results on benchmark data show that the proposed feature subspace ensembles method consistently leads to improved classification performance.