A self-training semi-supervised classification algorithm based on density peaks of data and differential evolution

doi:10.1109/ICNSC.2018.8361359

	A self-training semi-supervised classification algorithm based on density peaks of data and differential evolution
	Wu, Di1 ; Shang, Mingsheng1 ; Wang, Guoyin 1; Li, Li 2
	2018
会议日期	March 27, 2018 - March 29, 2018
会议地点	Zhuhai, China
DOI	10.1109/ICNSC.2018.8361359
页码	1-6
英文摘要	Self-training semi-supervised classification methodology is highly effective in alleviating the shortage of labeled data in classification tasks via an iterative self-training process. In this paper, we propose a self-training semi-supervised classification algorithm based on density peaks of data and differential evolution. The proposed algorithm consists of two main parts. First part is to use the underlying structure of data space, which is discovered based on density peaks of data, to help train a better classifier. Second part is to use the differential evolution to optimize the positioning of newly labeled data during the self-training process, where newly labeled data denotes the unlabeled data labeled by classifier during the self-training process and optimizing the positioning means optimally adjusting the attributes values of date. Experimental results on 12 benchmark datasets clearly demonstrate that the proposed algorithm is more effective than some previous works in improving the performance of base classifier of support vector machine or k-nearest neighbor. © 2018 IEEE.
会议录	15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018
语种	英语
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/7953]
专题	中国科学院重庆绿色智能技术研究院
作者单位	1.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China; 2.Chongqing Geomatic Center, Chongqing; 401121, China
推荐引用方式 GB/T 7714	Wu, Di,Shang, Mingsheng,Wang, Guoyin,et al. A self-training semi-supervised classification algorithm based on density peaks of data and differential evolution[C]. 见:. Zhuhai, China. March 27, 2018 - March 29, 2018.