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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Feng Yang Mao, K.Z. |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore (Feng Yang; Mao, K.Z.) |
| Abstract | Feature ranking, which ranks features via their individual importance, is one of the frequently used feature selection techniques. Traditional feature ranking criteria are apt to produce inconsistent ranking results even with light perturbations in training samples when applied to high dimensional and small-sized gene expression data. A widely used strategy for solving the inconsistencies is the multi-criterion combination. But one problem encountered in combining multiple criteria is the score normalization. In this paper, problems in existing methods are first analyzed, and a new gene importance transformation algorithm is then proposed. Experimental studies on three popular gene expression datasets show that the multi-criterion combination based on the proposed score correction and normalization produces gene rankings with improved robustness. |
| Starting Page | 444 |
| Ending Page | 449 |
| File Size | 479209 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424483068 |
| e-ISBN | 9781424483075 |
| DOI | 10.1109/BIBM.2010.5706607 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Economic indicators robustness multi-criterion Classification algorithms Gene expression score normalization Support vector machines feature Training data Robustness ranking Colon combination |
| Content Type | Text |
| Resource Type | Article |
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