A Wrapper for Feature Selection Based on Mutual Information

Authors:
Jinjie Huang;Yunze Cai;Xiaoming Xu
Affiliations:
Shanghai Jiao Tong University, China;Shanghai Jiao Tong University, China;Shanghai Jiao Tong University, China
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

Citing 0
Cited 5

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An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine

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Abstract

This paper adopts a wrapper method to find a subset of features that are most relevant to the classification task. The approach utilizes an improved estimation of the conditional mutual information which is used as an independent measure for feature ranking in the local search operations. Meanwhile, the mutual information between the predictive labels of a trained classifier and the true classes is used as the fitness function in the global search for the best subset of features. Thus, the local and global searches consist of a hybrid genetic algorithm for feature selection. Experimental results demonstrate both parsimonious feature selection and excellent classification accuracy of the method on a range of benchmark data sets.