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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wenxing Li Jian Hou Lizhi Yin |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Inf. Sci. & Technol., Bohai Univ., Jinzhou, China (Jian Hou) || Coll. of Eng., Bohai Univ., Jinzhou, China (Wenxing Li; Lizhi Yin) |
| Abstract | Classifier fusion methods are usually used to combine multiple classification decisions and generate better classification results than any single classifier. In order to improve object classification accuracy, it is a common method to assign weights to classifiers based on their importance in a multiple decision system. In this paper we put forward a method to weight different classifiers in classifier fusion. With SVM as the classifier, we use cross-validation to estimate the accuracy of classifiers and therefore the importance of classifiers in classifier fusion. In the next step we test different weighting methods based on the classifier accuracy and select the best performing one. In experiments on three diverse datasets, our method performs better than average fusion and the best single classifier, and also comparable to the state-of-the-art methods in literature. |
| Starting Page | 2119 |
| Ending Page | 2122 |
| File Size | 99427 |
| Page Count | 4 |
| File Format | |
| e-ISBN | 9781479925384 |
| DOI | 10.1109/ICMC.2014.7231940 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-07-03 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training Computer vision Accuracy Conferences Pattern recognition Kernel |
| Content Type | Text |
| Resource Type | Article |
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