抄録
Classification analysis is one of core research topics in pattern recognition field. According to the distribution of samples, algorithms like artificial network (ANN) and support vector machine (SVM) have been proposed to perform binary classification. But these traditional classification algorithms hardly work well for imbalanced dataset. In this study, a novel model on the basis of memetic algorithm (MA) and support vector machine (SVM) is proposed to perform the classification for large imbalanced dataset. It is named MSVC (memetic support vector classification) model. Memetic Algorithm is recently proposed and used as a heuristic framework for the large imbalanced classification. Because of the high performance of SVM in balanced binary classification, support vector classification (SVC) is combined with MA to improve the classification accuracy. G-mean is used to check the final result. Compared with some conventional models, the results showed that this model is a proper alternative for imbalanced dataset classification, and it expends the applications of memetic algorithm.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011 |
| ページ | 389-392 |
| ページ数 | 4 |
| DOI | |
| 出版物ステータス | Published - 2011 |
| イベント | 5th International Conference on Genetic and Evolutionary Computing, ICGEC2011 - Xiamen 継続期間: 2011 8 29 → 2011 9 1 |
Other
| Other | 5th International Conference on Genetic and Evolutionary Computing, ICGEC2011 |
|---|---|
| 市 | Xiamen |
| 期間 | 11/8/29 → 11/9/1 |
Fingerprint
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
これを引用
Building a memetic algorithm based support vector machine for imbalaced classification. / Mingnan, Wu; Watada, Junzo; Ibrahim, Zuwarie; Khalid, Marzuki.
Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011. 2011. p. 389-392 6042808.研究成果: Conference contribution
}
TY - GEN
T1 - Building a memetic algorithm based support vector machine for imbalaced classification
AU - Mingnan, Wu
AU - Watada, Junzo
AU - Ibrahim, Zuwarie
AU - Khalid, Marzuki
PY - 2011
Y1 - 2011
N2 - Classification analysis is one of core research topics in pattern recognition field. According to the distribution of samples, algorithms like artificial network (ANN) and support vector machine (SVM) have been proposed to perform binary classification. But these traditional classification algorithms hardly work well for imbalanced dataset. In this study, a novel model on the basis of memetic algorithm (MA) and support vector machine (SVM) is proposed to perform the classification for large imbalanced dataset. It is named MSVC (memetic support vector classification) model. Memetic Algorithm is recently proposed and used as a heuristic framework for the large imbalanced classification. Because of the high performance of SVM in balanced binary classification, support vector classification (SVC) is combined with MA to improve the classification accuracy. G-mean is used to check the final result. Compared with some conventional models, the results showed that this model is a proper alternative for imbalanced dataset classification, and it expends the applications of memetic algorithm.
AB - Classification analysis is one of core research topics in pattern recognition field. According to the distribution of samples, algorithms like artificial network (ANN) and support vector machine (SVM) have been proposed to perform binary classification. But these traditional classification algorithms hardly work well for imbalanced dataset. In this study, a novel model on the basis of memetic algorithm (MA) and support vector machine (SVM) is proposed to perform the classification for large imbalanced dataset. It is named MSVC (memetic support vector classification) model. Memetic Algorithm is recently proposed and used as a heuristic framework for the large imbalanced classification. Because of the high performance of SVM in balanced binary classification, support vector classification (SVC) is combined with MA to improve the classification accuracy. G-mean is used to check the final result. Compared with some conventional models, the results showed that this model is a proper alternative for imbalanced dataset classification, and it expends the applications of memetic algorithm.
KW - Classification on imbalanced dataset
KW - Memetic algorithm (MA)
KW - Memetic support vector classification (MSVC)
KW - Support vector machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=80455137177&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80455137177&partnerID=8YFLogxK
U2 - 10.1109/ICGEC.2011.95
DO - 10.1109/ICGEC.2011.95
M3 - Conference contribution
AN - SCOPUS:80455137177
SN - 9780769544496
SP - 389
EP - 392
BT - Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011
ER -