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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jianwen Sun Zongkai Yang Pei Wang Lin Liu Sanya Liu |
| Copyright Year | 2010 |
| Abstract | One major task of online writeprint identification is to select the key features for representing the writeprint and facilitating the classifier built by using only the selected feature subset. In this study, we develop a hybrid genetic algorithm: RelieF Fed Genetic Algorithm (RFGA) which incorporates feature weight information produced by using RelieF as the heuristic to identity the key features and improve the identification performance. Experiments are conducted on a test bed encompassing hundreds of reviews posted by 20 Amazon customers to examine the method. The experimental results using RFGA show the proposed approach is effective, obtaining a significant improvement in performance, with satisfactory classification accuracy of 96.67%, and having a heavy reduction in feature dimensionality that is only 3% of the no feature selection baseline. |
| Starting Page | 76 |
| Ending Page | 79 |
| File Size | 689362 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424480944 |
| DOI | 10.1109/ISCID.2010.28 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-10-29 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Radio frequency Training Accuracy Gallium hybrid genetic algorithm Feature extraction Classification algorithms online writeprint identification Biological cells feature selection |
| Content Type | Text |
| Resource Type | Article |
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