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http://dx.doi.org/10.7737/JKORMS.2014.39.4.075

Set Covering-based Feature Selection of Large-scale Omics Data  

Ma, Zhengyu (School of Industrial Management Engineering, Korea University)
Yan, Kedong (School of Information Management Engineering, Korea University)
Kim, Kwangsoo (Bioinformatics Institute, Seoul National University)
Ryoo, Hong Seo (School of Industrial Management Engineering, Korea University)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.39, no.4, 2014 , pp. 75-84 More about this Journal
Abstract
In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.
Keywords
Bioinformatics; Feature Selection; Set Covering Problem; Omics Data;
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