title: An ensemble of SVM classifiers based on gene pairs.
abstract: In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets.
authors.affiliation: Department of Electrical Engineering, Xiamen University, 361005 Xiamen, Fujian, China.
authors.forename: Muchenxuan
authors.initials: M
authors.lastname: Tong
authors.forename: Kun-Hong
authors.initials: KH
authors.lastname: Liu
authors.forename: Chungui
authors.initials: C
authors.lastname: Xu
authors.forename: Wenbin
authors.initials: W
authors.lastname: Ju
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D005796:Genes; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D060388:Support Vector Machine; D059467:Transcriptome