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Feature Extraction Based on Decision Boundaries
April 1993 (vol. 15 no. 4)
pp. 388-400

A novel approach to feature extraction for classification based directly on the decision boundaries is proposed. It is shown how discriminantly redundant features and discriminantly informative features are related to decision boundaries. A procedure to extract discriminantly informative features based on a decision boundary is proposed. The proposed feature extraction algorithm has several desirable properties: (1) it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal class means or equal class covariances as some previous algorithms do. Experiments show that the performance of the proposed algorithm compares favorably with those of previous algorithms.

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Index Terms:
Bayes methods; decision boundaries; feature extraction; classification; discriminantly redundant features; discriminantly informative features; pattern recognition; Bayes methods; decision theory; feature extraction
Citation:
C. Lee, D.A. Landgrebe, "Feature Extraction Based on Decision Boundaries," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 4, pp. 388-400, April 1993, doi:10.1109/34.206958
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