Various classification algorithms became available due to a surge of interdisciplinary research interests in the areas of data mining and knowledge discovery. We develop a statistical meta-model which compares the classification performances of several algorithms in terms of data characteristics. This empirical model is expected to aid decision making processes of finding the best classification tool in the sense of providing the minimum classification error among alternatives.
|Number of pages||8|
|Journal||IEEE transactions on pattern analysis and machine intelligence|
|Publication status||Published - 1999|
Bibliographical noteFunding Information:
This work was partly supported by the Yonsei University Research Fund of 1997 and by grant no, 1999-1-303-005-3 from the Interdisciplinary Research Program of the KOSEF..
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics