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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wanyu Deng Qinghua Zheng Yulan Wang Lin Chen Xuebin Xu |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. Sci. & Technol., Xi'an Jiaotong Univ., Xi'an (Qinghua Zheng; Xuebin Xu) || MOE KLINNS Lab., Xian Jiaotong Univ., Xian (Wanyu Deng) || Dept. of Comput. Sci. & Technol., Inst. of Post & Telecommuniction of Xi'an, Xi'an (Lin Chen) || Sch. of Foreign Language, Xi'an Jiaotong Univ., Xi'an (Yulan Wang) |
| Abstract | Naive Bayes (NB) based on the attribute independence assumption has been widely applied in many domains for its simplicity and efficiency. However, the independence assumption is often violated in many real-world applications. In response to this problem, a mount of research has been carried out to improve NBpsilas accuracy by mitigating the attribute independence assumption, for example Lazy learning of Bayesian Rules(LBR), Tree Augmented Naive Bayes (TAN) and Averaged One-Dependence Estimator(AODE). AODE which averages all Super Parent One-dependence Estimators (SPODE) has attracted widely attention for its outstanding performance. Because of the different role of every SPODEs, the performance will be expected to be improved significantly if different weights are assigned to these SPODEs. We proposed the framework of linear weighted SPODE ensemble and efficient learning strategy of weights based on differential evolution. The experience has shown that the proposed algorithm can generate better performance in most case than NB, AODE, WAODE, TAN and LBR. |
| Starting Page | 191 |
| Ending Page | 195 |
| File Size | 305015 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424425129 |
| DOI | 10.1109/GRC.2008.4664679 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-08-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Learning Differential Evolutionary AODE Naïve Bayes Evolution (biology) Bayesian methods Machine learning Classification algorithms Classifier Niobium Generic Algorithm |
| Content Type | Text |
| Resource Type | Article |
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