title: Optimal combination of feature selection and classification via local hyperplane based learning strategy.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Computer Science& Engineering, South China University of Technology, Guangdong, China. c.xp01@mail.scut.edu.cn.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Cheng
authors.affiliation: School of Computer Science& Engineering, South China University of Technology, Guangdong, China. hmcai@scut.edu.cn.
authors.forename: Hongmin
authors.initials: H
authors.lastname: Cai
authors.affiliation: Electrical And Information College of Jinan University, Guangdong, China. yuezhang@comp.hkbu.edu.hk.|BNU-HKBU United International College, Hong Kong, China. yuezhang@comp.hkbu.edu.hk.
authors.forename: Yue
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Computer Science& Engineering, South China University of Technology, Guangdong, China. xubo_2009@163.com.
authors.forename: Bo
authors.initials: B
authors.lastname: Xu
authors.affiliation: BNU-HKBU United International College, Hong Kong, China. wfsu@uic.edu.hk.
authors.forename: Weifeng
authors.initials: W
authors.lastname: Su
mesh_terms: D016000:Cluster Analysis; D016002:Discriminant Analysis; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D000069550:Machine Learning / Q000592:standards*; D009369:Neoplasms / Q000145:classification* / Q000235:genetics*; D060388:Support Vector Machine