title: Genetic programming based ensemble system for microarray data classification.
abstract: 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.
authors: Software School of Xiamen University, Xiamen, Fujian 361005, China ; Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong.
authors: Kun-Hong
authors: KH
authors: Liu
authors: Baidu Inc., Beijing 100000, China.
authors: Muchenxuan
authors: M
authors: Tong
authors: School of Computer Engineering, Jimei University, Xiamen, Fujian 361021, China.
authors: Shu-Tong
authors: ST
authors: Xie
authors: Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong.
authors: Vincent To
authors: VT
authors: Yee Ng
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis* / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results