Feature Selection Based on Mutual Correlation

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 Bhattacharyya 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 i...

• Type: Conference
• Year: 2006
• Where: CIARP
• Authors: Michal Haindl, Petr Somol, Dimitrios Ververidis, Constantine Kotropoulos
• Keywords: CIARP 2006, Feature Selection, Filter Method, Pattern Recognition, Wrapper Methods