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Optimizing feature extraction for multiclass problems | IEEE Journals & Magazine | IEEE Xplore

Optimizing feature extraction for multiclass problems


Abstract:

Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature e...Show More

Abstract:

Feature extraction has been an important research topic in pattern classification and has been studied extensively by many researchers. Most of the conventional feature extraction methods are performed using a criterion function defined between two classes or a global function. Although these methods work relatively well in most cases, it is generally not optimal in any sense for multiclass problems. In order to address this problem, the authors propose a method to optimize feature extraction for multiclass problems. The authors first investigate the distribution of classification accuracies of multiclass problems in the feature space and find that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then the authors propose an algorithm that finds such features. Experiments with remotely sensed data show that the proposed algorithm consistently provides better performances compared with the conventional feature extraction algorithms.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 39, Issue: 3, March 2001)
Page(s): 521 - 528
Date of Publication: 31 March 2001

ISSN Information:


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