McTwo: a two-step feature selection algorithm based on maximal information coefficient
文献类型：期刊论文
| 作者 | Ruiquan Ge; Manli Zhou; Youxi Luo; Qinghan Meng; Guoqin Mai; Dongli Ma; Guoqing Wang; Fengfeng Zhou |
| 刊名 | BMC Bioinformatics
 |
| 出版日期 | 2016 |
| 英文摘要 | Background High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This “large p, small n” paradigm in the area of biomedical “big data” may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets. Results This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature. Conclusion McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets. |
| 收录类别 | SCI |
| 原文出处 | http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0990-0 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10393]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | BMC Bioinformatics |
| 推荐引用方式 GB/T 7714 | Ruiquan Ge,Manli Zhou,Youxi Luo,et al. McTwo: a two-step feature selection algorithm based on maximal information coefficient[J]. BMC Bioinformatics,2016. |
| APA | Ruiquan Ge.,Manli Zhou.,Youxi Luo.,Qinghan Meng.,Guoqin Mai.,...&Fengfeng Zhou.(2016).McTwo: a two-step feature selection algorithm based on maximal information coefficient.BMC Bioinformatics. |
| MLA | Ruiquan Ge,et al."McTwo: a two-step feature selection algorithm based on maximal information coefficient".BMC Bioinformatics (2016). |
入库方式： OAI收割
来源：深圳先进技术研究院
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