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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yongqiang Dai Bin Hu Yun Su Chengsheng Mao Jing Chen Xiaowei Zhang Moore, P. Lixin Xu Hanshu Cai |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China (Yongqiang Dai; Bin Hu; Yun Su; Chengsheng Mao; Jing Chen; Xiaowei Zhang; Moore, P.; Lixin Xu; Hanshu Cai) |
| Abstract | High-dimensional biomedical datasets contain thousands of features used in molecular disease diagnosis, however many irrelevant or weak correlation features influence the predictive accuracy. Feature selection algorithms enable classification techniques to accurately identify patterns in the features and find a feature subset from an original set of features without reducing the predictive classification accuracy while reducing the computational overhead in data mining. In this paper we present an improved shuffled frog leaping algorithm (ISFLA) which explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. Evaluation employs the K-nearest neighbour approach and a comparative analysis with a genetic algorithm, particle swarm optimization and the shuffled frog leaping algorithm shows that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy. |
| Sponsorship | IEEE |
| Starting Page | 458 |
| Ending Page | 463 |
| File Size | 2101274 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781467367998 |
| DOI | 10.1109/BIBM.2015.7359728 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-11-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis biomedical data Biology KNN classification accuracy SFLA feature selection Diseases |
| Content Type | Text |
| Resource Type | Article |
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