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Input variable selection for feature extraction in classification problems
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DOI：10.1016/j.sigpro.2011.08.023.png)
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Abstract
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We propose an input variable selection method based on discriminant features. By analyzing the relationship between the input space and feature space obtained by discriminant analysis, the input variables that contain a large amount of discriminative information are selected, while input variables with less discriminative information are discarded. By this, the signal to noise ratio of the data can be improved. The proposed method can be applied not only to the feature extraction methods based on covariance matrix but also to the methods based on image covariance matrix. The experimental results obtained with various data sets show that the proposed method results in improved classification performance regardless of the dimension and type of data. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Input variable selection
Pattern classification
Feature extraction
Linear discriminant analysis
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Invariant optimal feature selection: A distance discriminant and feature ranking based solution.png)
PATTERN RECOGNITION
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PATTERN RECOGNITIONIF7.6
OAAIShareSaveFast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognitionSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
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