Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1137-1144 |
| Number of pages | 8 |
| Journal | IEEE transactions on pattern analysis and machine intelligence |
| Volume | 21 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 1999 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics
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Meta analysis of classification algorithms for pattern recognition. / Sohn, So Young.
In: IEEE transactions on pattern analysis and machine intelligence, Vol. 21, No. 11, 01.12.1999, p. 1137-1144.Research output: Contribution to journal › Article
TY - JOUR
T1 - Meta analysis of classification algorithms for pattern recognition
AU - Sohn, So Young
PY - 1999/12/1
Y1 - 1999/12/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=0033220832&partnerID=8YFLogxK
U2 - 10.1109/34.809107
DO - 10.1109/34.809107
M3 - Article
AN - SCOPUS:0033220832
VL - 21
SP - 1137
EP - 1144
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
SN - 0162-8828
IS - 11
ER -