AUTOMATIC CONSTRUCTION OF A HIERARCHICAL CLASSIFIER |
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| D. Filbert, F. Attia, R. Jahnke |
- Abstract:
- The automatic construction of a hierarchical classifier is described. The construction process uses the same classifier to select the features, which is used later for the classification itself. The construction leads to a binary decision tree. Every node is labelled with a feature vector.
The classical statistical approach to feature selection is presented. The add-on algorithm provides feature vectors of minimal length without regarding the classifier. Two applications are described and the results, reached by the classical statistical algorithm and the new hierarchical classifier, are compared. - Keywords:
- Decision tree, supervised learning, classification, feature evaluation
- Download:
- IMEKO-WC-2000-TC10-P262.pdf
- DOI:
- -
- Event details
- Event name:
- XVI IMEKO World Congress
- Title:
Measurement - Supports Science - Improves Technology - Protects Environment ... and Provides Employment - Now and in the Future
- Place:
- Vienna, AUSTRIA
- Time:
- 25 September 2000 - 28 September 2000