Combined SVM-Based Feature Selection and Classification
Feature selection is an important combinatorial optimisation problem in the context of supervised pattern classification. This paper presents four novel continuous feature selection approaches directly minimising the classifier performance. In particular, we include linear and nonlinear Support Vect...
Gespeichert in:
| Veröffentlicht in: | Machine learning Jg. 61; H. 1-3; S. 129 - 150 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Dordrecht
Springer Nature B.V
01.11.2005
|
| Schlagworte: | |
| ISSN: | 0885-6125, 1573-0565 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Zusammenfassung: | Feature selection is an important combinatorial optimisation problem in the context of supervised pattern classification. This paper presents four novel continuous feature selection approaches directly minimising the classifier performance. In particular, we include linear and nonlinear Support Vector Machine classifiers. The key ideas of our approaches are additional regularisation and embedded nonlinear feature selection. To solve our optimisation problems, we apply difference of convex functions programming which is a general framework for non-convex continuous optimisation. Experiments with artificial data and with various real-world problems including organ classification in computed tomography scans demonstrate that our methods accomplish the desired feature selection and classification performance simultaneously.[PUBLICATION ABSTRACT] |
|---|---|
| Bibliographie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 |
| ISSN: | 0885-6125 1573-0565 |
| DOI: | 10.1007/s10994-005-1505-9 |