Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Perception of stress and speaking style for selected elements of the SUSAS database
Speech Communication
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
A dimensionality reduction based on feature quality measure
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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Feature selection is a critical procedure in many pattern recognition applications. There are two distinct mechanisms for feature selection namely the wrapper methods and the filter methods. The filter methods are generally considered inferior to wrapper methods, however wrapper methods are computationally more demanding than filter methods. A novel filter feature selection method based on mutual correlation is proposed. We assess the classification performance of the proposed filter method by using the selected features to the Bayes classifier. Alternative filter feature selection methods that optimize either the Bhattacharrrya distance or the divergence are also tested. Furthermore, wrapper feature selection techniques employing several search strategies such as the sequential forward search, the oscillating search, and the sequential floating forward search are also included in the comparative study. A trade off between the classification accuracy and the feature set dimensionality is demonstrated on both two benchmark datasets from UCI repository and two emotional speech data collections.