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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ramos-Jimenez, G. del Campo-Avila, J. Morales-Bueno, R. |
| Copyright Year | 2009 |
| Abstract | Two extensive research areas in Machine Learning are classification and prediction. Many approaches have been focused in the induction of ensemble to increase learning accuracy of individual classifiers. Recently, new approaches, different to those that look for accurate and diverse base classifiers, are emerging. In this paper we present a system made up of two layers: in the first layer, one ensemble classifier process every example and tries to classify them; in the second layer, one individual classifier is induced using the examples that are not unanimously classified by the ensemble. In addition, the examples that reach to the second layer incorporate new information added in the ensemble. Thus, we can achieve some improvement in the accuracy level, because the second layer can do more informed classifications. In the experimental section we present some results that suggest that our proposal can actually improve the accuracy of the system. |
| Starting Page | 199 |
| Ending Page | 202 |
| File Size | 283303 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424447350 |
| DOI | 10.1109/ISDA.2009.148 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-30 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | ensemble classifiers Voting Machine learning many-layered learning hybrid learning Decision trees Size control Proposals Hybrid intelligent systems Classification tree analysis |
| Content Type | Text |
| Resource Type | Article |
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