发表状态 | 已发表Published |
题名 | Optimal combination of feature selection and classification via local hyperplane based learning strategy |
作者 | |
发表日期 | 2015-07-10 |
发表期刊 | BMC Bioinformatics
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卷号 | 16期号:1 |
摘要 | Background: Classifying cancers by gene selection is among the most important and challenging procedures in biomedicine. A major challenge is to design an effective method that eliminates irrelevant, redundant, or noisy genes from the classification, while retaining all of the highly discriminative genes. Results: We propose a gene selection method, called local hyperplane-based discriminant analysis (LHDA). LHDA adopts two central ideas. First, it uses a local approximation rather than global measurement; second, it embeds a recently reported classification model, K-Local Hyperplane Distance Nearest Neighbor(HKNN) classifier, into its discriminator. Through classification accuracy-based iterations, LHDA obtains the feature weight vector and finally extracts the optimal feature subset. The performance of the proposed method is evaluated in extensive experiments on synthetic and real microarray benchmark datasets. Eight classical feature selection methods, four classification models and two popular embedded learning schemes, including k-nearest neighbor (KNN), hyperplane k-nearest neighbor (HKNN), Support Vector Machine (SVM) and Random Forest are employed for comparisons. Conclusion: The proposed method yielded comparable to or superior performances to seven state-of-the-art models. The nice performance demonstrate the superiority of combining feature weighting with model learning into an unified framework to achieve the two tasks simultaneously. |
关键词 | Classification Feature weighting HKNN Local hyperplane Local learning |
DOI | 10.1186/s12859-015-0629-6 |
URL | 查看来源 |
收录类别 | SCIE |
语种 | 英语English |
WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
WOS类目 | Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
WOS记录号 | WOS:000357631800002 |
Scopus入藏号 | 2-s2.0-85019235741 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/6449 |
专题 | 理工科技学院 |
通讯作者 | Cai, Hongmin |
作者单位 | 1.School of Computer Science and Engineering, South China University of Technology,Guangdong,China 2.Electrical And Information College of Jinan University,Guangdong,China 3.BNU-HKBU United International College,Zhuhai,China |
推荐引用方式 GB/T 7714 | Cheng, Xiaoping,Cai, Hongmin,Zhang, Yueet al. Optimal combination of feature selection and classification via local hyperplane based learning strategy[J]. BMC Bioinformatics, 2015, 16(1). |
APA | Cheng, Xiaoping, Cai, Hongmin, Zhang, Yue, Xu, Bo, & Su, Weifeng. (2015). Optimal combination of feature selection and classification via local hyperplane based learning strategy. BMC Bioinformatics, 16(1). |
MLA | Cheng, Xiaoping,et al."Optimal combination of feature selection and classification via local hyperplane based learning strategy". BMC Bioinformatics 16.1(2015). |
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