title: New feature selection for gene expression classification based on degree of class overlap in principal dimensions.
abstract: Micro-array data are typically characterized by high dimensional features with a small number of samples. Several problems in identifying genes causing diseases from micro-array data can be transformed into the problem of classifying the features extracted from gene expression in micro-array data. However, too many features can cause low prediction accuracy as well as high computational complexity. Dimensional reduction is a method to eliminate irrelevant features to improve the prediction accuracy. Typically, the eigenvalues or dimensional data variance from principal component analysis are used as criteria to select relevant features. This approach is simple but not efficient since it does not concern the degree of data overlap in each dimension in the feature space. A new method to select relevant features based on degree of dimensional data overlap with proper feature selection was introduced. Furthermore, our study concentrated on small sized data sets which usually occur in reality. The experimental results signified that this new approach can achieve substantially higher prediction accuracy when compared with other methods.
authors.affiliation: Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Patumwan, Bangkok 10330, Thailand. Electronic address: somsak.rak@gmail.com.
authors.forename: Somsak
authors.initials: S
authors.lastname: Rakkeitwinai
authors.affiliation: Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Patumwan, Bangkok 10330, Thailand.
authors.forename: Chidchanok
authors.initials: C
authors.lastname: Lursinsap
authors.affiliation: Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Patumwan, Bangkok 10330, Thailand.
authors.forename: Chatchawit
authors.initials: C
authors.lastname: Aporntewan
authors.affiliation: Department of Anatomy, Faculty of Medicine, Chulalongkorn University, Patumwan, Bangkok 10330, Thailand.
authors.forename: Apiwat
authors.initials: A
authors.lastname: Mutirangura
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000145:classification* / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D025341:Principal Component Analysis; D012372:ROC Curve; D060388:Support Vector Machine
keywords: Analysis; Dimension reduction; Feature extraction; Feature selection; Micro-array data; Support Vector Machine; principal component