Inductive vs transductive inference, global vs local models: SVM, TSVM, and SVMT for gene expression classification problems
| dc.contributor.author | Pang, S. | |
| dc.contributor.author | Kasabov, N | |
| dc.date.accessioned | 2009-05-27T22:18:57Z | |
| dc.date.available | 2009-05-27T22:18:57Z | |
| dc.date.copyright | 2004 | |
| dc.date.created | 2004 | |
| dc.date.issued | 2004 | |
| dc.description.abstract | This paper compares inductive-, versus transductive modeling, and also global-, versus local models with the use of SVM for gene expression classification problems. SVM are used in their three variants - inductive SVM, transductive SVM (TSVM), and SVM tree (SVMT) -the last two techniques being recently introduced by the authors. The problem of gene expression classification is used for illustration and four benchmark data sets are used to compare the different SVM methods. The TSVM outperforms the inductive SVM models applied on a small to medium variable (gene) set and a small to medium sample set, while SVMT is superior when the problem is defined with a large data set, or - a large set of variables (e.g. 7,000 genes, with little or no variable pre-selection). | |
| dc.identifier.doi | 10.1109/IJCNN.2004.1380112 | |
| dc.identifier.uri | https://hdl.handle.net/10292/618 | |
| dc.publisher | IEEE | |
| dc.rights | ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | |
| dc.rights.accessrights | OpenAccess | |
| dc.source | IEEE International Conference on Neural Networks, 2, 1197-1202 | |
| dc.title | Inductive vs transductive inference, global vs local models: SVM, TSVM, and SVMT for gene expression classification problems | |
| dc.type | Conference Proceedings |