Abstract
This paper proposed a hybrid functional link artificial neural network (HFLANN) embedded with an optimization of input features for solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded selected features, HFLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer neural networks. An extensive simulation studies has been carried out to illustrate the effectiveness of this method over to its rival functional link artificial neural network (FLANN) and radial basis function (RBF) neural network.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings |
| Pages | 156-163 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2008 Dec 31 |
| Event | 9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008 - Daejeon, Korea, Republic of Duration: 2008 Nov 2 → 2008 Nov 5 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5326 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008 |
|---|---|
| Country | Korea, Republic of |
| City | Daejeon |
| Period | 08/11/2 → 08/11/5 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
Genetic feature selection for optimal functional link artificial neural network in classification. / Dehuri, Satchidananda; Mishra, Bijan Bihari; Cho, Sung Bae.
Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings. 2008. p. 156-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5326 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Genetic feature selection for optimal functional link artificial neural network in classification
AU - Dehuri, Satchidananda
AU - Mishra, Bijan Bihari
AU - Cho, Sung Bae
PY - 2008/12/31
Y1 - 2008/12/31
N2 - This paper proposed a hybrid functional link artificial neural network (HFLANN) embedded with an optimization of input features for solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded selected features, HFLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer neural networks. An extensive simulation studies has been carried out to illustrate the effectiveness of this method over to its rival functional link artificial neural network (FLANN) and radial basis function (RBF) neural network.
AB - This paper proposed a hybrid functional link artificial neural network (HFLANN) embedded with an optimization of input features for solving the problem of classification in data mining. The aim of the proposed approach is to choose an optimal subset of input features using genetic algorithm by eliminating features with little or no predictive information and increase the comprehensibility of resulting HFLANN. Using the functionally expanded selected features, HFLANN overcomes the non-linearity nature of problems, which is commonly encountered in single layer neural networks. An extensive simulation studies has been carried out to illustrate the effectiveness of this method over to its rival functional link artificial neural network (FLANN) and radial basis function (RBF) neural network.
UR - http://www.scopus.com/inward/record.url?scp=58049124482&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58049124482&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-88906-9-20
DO - 10.1007/978-3-540-88906-9-20
M3 - Conference contribution
AN - SCOPUS:58049124482
SN - 3540889051
SN - 9783540889052
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 156
EP - 163
BT - Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings
ER -