Optimizing feature extraction for multiclass cases

Chulhee Lee, Joonyong Hong

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


In this paper, we propose a new optimization method for multiclass feature extraction problems by assigning weights to each class in computing the global criterion function and adjusting the weights as new features are extracted. Recently, it is shown that it is possible to predict the classification error within 1-2% margin from the Bhattacharyya distance. We use the error prediction technique to adjust the weights of each classes. Initially, we assign equal weights to each class. After the first feature is extracted, we calculate classification error of each class when the first feature is used and adjust the weights accordingly. We compute again the global criterion function with a new set of weights excluding the first feature and calculate the second feature from the revised criterion function, and so on. Preliminary experiments show improvement over the conventional methods.

Original languageEnglish
Pages (from-to)2545-2548
Number of pages4
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 1997
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA
Duration: 1997 Oct 121997 Oct 15

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture


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