Multiobjective feature selection for microarray data via distributed parallel algorithms

作     者：Cao, Bin Zhao, Jianwei Yang, Po Yang, Peng Liu, Xin Qi, Jun Simpson, Andrew Elhoseny, Mohamed Mehmoode, Irfan Muhammad, Khan 

作者机构：Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Liverpool John Moores Univ Dept Comp Sci Liverpool Merseyside England Mansoura Univ Fac Computers & Informat Mansoura Egypt Univ Bradford Dept Media Design & Technol Fac Engn & Informat Bradford BD7 1DP W Yorkshire England Sejong Univ Dept Software Seoul 143747 South Korea 

出 版 物：《FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE》 (下代计算机系统)

年 卷 期：2019年第100卷

页      面：952-981页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University, China Opening Project of Guangdong High Performance Computing Society, China Foundation of Key Laboratory of Machine Intelligence and Advanced Computing of the Ministry of Education, China [MSC-201602A] Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund, China [U1501501] 

主　　题：Microarray dataset High dimension Multiobjective feature selection Distributed parallelism Feature redundancy 

摘      要：Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Through feature selection, a feature subset that contains only a small quantity of essential features can be generated to increase the classification accuracy and significantly reduce the time consumption. In this paper, we construct a multiobjective feature selection model that simultaneously considers the classification error, the feature number and the feature redundancy. For this model, we propose several distributed parallel algorithms based on different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including a feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency. (C) 2019 Published by Elsevier B.V.