Abstract
Low dimensional representation of multivariate data using unsupervised feature extraction is combined with a hybrid feature selection method to improve classification performance of recognition tasks. The proposed hybrid feature selector is applied to the union of feature subspaces selected by Fisher criterion and feature-class mutual information (MI). It scores each feature as a linear weighted sum of its interclass MI and Fisher criterion score. Proposed method efficiently selects features with higher class discrimination in comparison to feature-class MI, Fisher criterion or unsupervised selection using variance; thus, resulting in much improved recognition performance. In addition, the paper also highlights the use of MI between a feature and class as a computationally economical and optimal feature selector for statistically independent features.
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Dhir, C.S., Lee, S.Y. (2009). Hybrid Feature Selection: Combining Fisher Criterion and Mutual Information for Efficient Feature Selection. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_75
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DOI: https://doi.org/10.1007/978-3-642-02490-0_75
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