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, we propose a method to optimize feature extraction for multiclass problems. We first investigated the distribution of classification accuracies of multiclass problems in the feature space and found that there exist much better feature sets that the conventional feature extraction algorithms fail to find. Then we proposed 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.
|Number of pages||8|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 2001 Mar|
Bibliographical noteFunding Information:
Manuscript received January 21, 2000; revised July 17, 2000. This work was supported in part by the Korean Science and Engineering Foundation. The authors are with the Department of Electrical and Computer Engineering, Yonsei University, Seoul 120-749, Korea firstname.lastname@example.org. Publisher Item Identifier S 0196-2892(01)02082-4.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)