Department of Electrical Engineering, Xiamen University, 361005 Xiamen, Fujian, China.
Comput Biol Med. 2013 Jul;43(6):729-37. doi: 10.1016/j.compbiomed.2013.03.010. Epub 2013 Mar 30.
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.
本文提出了一种基于基因对的遗传算法（GA）集成支持向量机（SVM）分类器（GA-ESP）。SVM（集成系统的基分类器）在不同的信息基因对上进行训练。这些基因对是由最高评分对（TSP）准则选择的。每一对都将原始微阵列表达映射到 2-D 空间。对基因对进行广泛的置换可能会揭示更多有用的信息，并有可能产生具有令人满意的准确性和可解释性的集成分类器。GA 进一步用于选择最优的基分类器组合。GA-ESP 分类器的有效性在二进制数据集和多类数据集上进行了评估。
