Semi-supervised SVM-based Feature Selection for Cancer Classification using Microarray Gene Expression Data

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• 关键词：Support vector machines ; Semi ; supervised ; Feature selection ; Cancer ; Gene expression
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9101
• 期：1
• 页码：468-477
• 全文大小：620 KB
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• 作者单位：Jun Chin Ang (9)
  Habibollah Haron (9)
  Haza Nuzly Abdull Hamed (9)
  
  9. Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
  
• 丛书名：Current Approaches in Applied Artificial Intelligence
• ISBN：978-3-319-19066-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Gene expression data always suffer from the high dimensionality issue, therefore feature selection becomes a fundamental tool in the analysis of cancer classification. Basically, the data can be collected easily without providing the label information, which is quite useful in improving the accuracy of the classification. Label information usually difficult to obtain as the labelling processes are tedious, costly and error prone. Previous studies of gene selection are mostly dedicated to supervised and unsupervised approaches. Support vector machine (SVM) is a common supervised technique to address gene selection and cancer classification problems. Hence, this paper aims to propose a semi-supervised SVM-based feature selection (S\(^3\)VM-FS), which simultaneously exploit the knowledge from unlabelled and labelled data. Experimental results on the gene expression data of lung cancer show that S\(^3\)VM-FS achieves the higher accuracy yet requires shorter processing time compares with the well-known supervised method, SVM-based recursive feature elimination (SVM-RFE) and the improved method, S\(^3\)VM-RFE.