Optimal combination of feature selection and classification via local hyperplane based learning strategy

BMC Bioinformatics

Methodology Article

Weifeng Su¹  Yue Zhang²  Xiaoping Cheng³  Hongmin Cai³  Bo Xu³ 

[1] BNU-HKBU United International College, Hong Kong, China;Electrical And Information College of Jinan University, Guangdong, China;BNU-HKBU United International College, Hong Kong, China;School of Computer Science& Engineering, South China University of Technology, Guangdong, China;

关键词: Feature weighting;    Local hyperplane;    Classification;    Local learning;    HKNN;   

DOI  :  10.1186/s12859-015-0629-6

 received in 2014-11-05, accepted in 2015-05-29,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundClassifying 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.ResultsWe 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.ConclusionThe 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.

【 授权许可】

Unknown   
© Cheng et al. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.