Liu Kun-Hong, Tong Muchenxuan, Xie Shu-Tong, Yee Ng Vincent To
Software School of Xiamen University, Xiamen, Fujian 361005, China ; Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong.
Baidu Inc., Beijing 100000, China.
Comput Math Methods Med. 2015;2015:193406. doi: 10.1155/2015/193406. Epub 2015 Feb 25.
Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.
近年来，越来越多的机器学习技术被应用于微阵列数据分析。本研究的目的是提出一种基于遗传编程（GP）的新型集成系统（名为GPES），该系统可用于有效分类不同类型的癌症。决策树在这个集成框架中作为基分类器，有三个算子：最小值、最大值和平均值。GP的每个个体都是一个集成系统，并且它们在进化过程中变得越来越准确。应用特征选择技术和平衡子采样技术来增加每个集成系统的多样性。最终的集成委员会通过前向搜索算法来选择，该算法显示能够自动拟合数据。使用五个二分类和六个多分类微阵列数据集对GPES的性能进行评估，结果表明，与其他一些集成系统相比，该算法在大多数情况下能取得更好的结果。通过使用精心设计的基分类器或应用其他采样技术，GPES的性能可能会进一步提高。
