Parallel Feature Selection Using Positive Approximation Based on MapReduce

摘要：

　　Over the last few decades,feature selection has been a hot research area in pattern recognition and machine learning,and many famous feature selection algorithms have been proposed.Among them,feature selection using positive approximation(FSPA)is an accelerator for traditional rough set based feature selection algorithms,which can significantly reduce the running time.However,FSPA still cannot handle large scale and high dimension dataset due to the memory constraints.In this paper,we propose a parallel implementation of FSPA using MapReduce framework,which is a programming model for processing large scale datasets.The experimental results demonstrate that the proposed algorithm can process large scale and high dimension dataset efficiently on commodity computers.

作者: Qing He Xiaohu Cheng Fuzhen Zhuang Zhongzhi Shi

作者单位: The Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese A The Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese A

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 405-410

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）