Abstract
Experiments show that CAIM discretization algorithm is superior to all the other top-down discretization algorithms. However, CAIM algorithm does not take the data distribution into account. The discretization formula used in CAIM also gives a high factor to the numbers of generated intervals. The two disadvantages make CAIM may generate irrational discrete results in some cases and further leads to the decrease of predictive accuracy of a classifier. In this paper we propose the Class-Attribute Contingency Coefficient discretization algorithm. The experimental results showed that compared with CAIM, our method can generate a better discretization scheme to bring on the improvement of accuracy of classification. With regard to the number of generated rules and execution time of a classifier, CACC and CAIM achieve comparable results.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007 |
| Pages | 472-476 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2007 Dec 1 |
| Event | 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007 - Haikou, China Duration: 2007 Aug 24 → 2007 Aug 27 |
Publication series
| Name | Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007 |
|---|---|
| Volume | 1 |
Other
| Other | 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007 |
|---|---|
| Country | China |
| City | Haikou |
| Period | 07-08-24 → 07-08-27 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
- Applied Mathematics
- Theoretical Computer Science
Cite this
}
A top-down and greedy method for discretization of continuous attributes. / Lee, Chien I.; Tsai, Cheng Jung; Yang, Ya Ru; Yang, Wei Pang.
Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007. 2007. p. 472-476 4405970 (Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A top-down and greedy method for discretization of continuous attributes
AU - Lee, Chien I.
AU - Tsai, Cheng Jung
AU - Yang, Ya Ru
AU - Yang, Wei Pang
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Experiments show that CAIM discretization algorithm is superior to all the other top-down discretization algorithms. However, CAIM algorithm does not take the data distribution into account. The discretization formula used in CAIM also gives a high factor to the numbers of generated intervals. The two disadvantages make CAIM may generate irrational discrete results in some cases and further leads to the decrease of predictive accuracy of a classifier. In this paper we propose the Class-Attribute Contingency Coefficient discretization algorithm. The experimental results showed that compared with CAIM, our method can generate a better discretization scheme to bring on the improvement of accuracy of classification. With regard to the number of generated rules and execution time of a classifier, CACC and CAIM achieve comparable results.
AB - Experiments show that CAIM discretization algorithm is superior to all the other top-down discretization algorithms. However, CAIM algorithm does not take the data distribution into account. The discretization formula used in CAIM also gives a high factor to the numbers of generated intervals. The two disadvantages make CAIM may generate irrational discrete results in some cases and further leads to the decrease of predictive accuracy of a classifier. In this paper we propose the Class-Attribute Contingency Coefficient discretization algorithm. The experimental results showed that compared with CAIM, our method can generate a better discretization scheme to bring on the improvement of accuracy of classification. With regard to the number of generated rules and execution time of a classifier, CACC and CAIM achieve comparable results.
UR - http://www.scopus.com/inward/record.url?scp=44049083520&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=44049083520&partnerID=8YFLogxK
U2 - 10.1109/FSKD.2007.129
DO - 10.1109/FSKD.2007.129
M3 - Conference contribution
AN - SCOPUS:44049083520
SN - 0769528740
SN - 9780769528748
T3 - Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007
SP - 472
EP - 476
BT - Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007
ER -