标题:A Classifier Chain Algorithm with K-means for Multi-label Classification on Clouds
作者:Yu, Zhilou; Hao, Hong; Zhang, Weipin; Dai, Hongjun
作者机构:[Yu, Zhilou] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China.; [Hao, Hong] Inspur Inc, Ctr Technol, Jinan 250101, 更多
通讯作者:Yu, Zhilou
通讯作者地址:[Yu, ZL]Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China.
来源:JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
出版年:2017
卷:86
期:2-3
页码:337-346
DOI:10.1007/s11265-016-1137-2
关键词:Big data analysis; Multi-label classification; Classifier chain; algorithm
摘要:It has become a basic precursor and facilitator to analyze the emergence of big data with the rise of cloud computing and cloud storage by means of the novel standardized technologies. Then, binary relevance method is carried out as one of the widely known classifier chain methods for multi-label classification. It achieves a higher predictive performance, but it still retains a complex process and takes much computation time. So, in this paper, we present a enhanced classifier chain algorithm with K-means cluster method to confirm the order of the binary classifiers. It has a different strategy that several times of K-means algorithms are employed to get the correlations between labels and to confirm the order of binary classifiers. The algorithm ensures the precise correlations to be transmitted persistently to improve the earlier predictions accuracy. The experiments on a sample data sets of Reuters-21578 show that the approach is effective and appealing in the common cases, it is accurate for a preliminary classification to provide a basis for the further refined classifications.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969902464&doi=10.1007%2fs11265-016-1137-2&partnerID=40&md5=8e59d6258cd8366a2c2d9b0551bf44e7
TOP