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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
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
DOI10.1186/s12859-015-0629-6
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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
被引频次:11[WOS]   [WOS记录]     [WOS相关记录]
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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