[1]SHI Yingzhong,WANG Shitong,DENG Zhaohong,et al.The core vector machine-based rapid classification of multi-task concept drift dataset[J].CAAI Transactions on Intelligent Systems,2018,13(6):935-945.[doi:10.11992/tis.201712019]
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The core vector machine-based rapid classification of multi-task concept drift dataset

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 6 Page number: 935-945 Column: 学术论文—机器学习 Public date: 2018-10-25
Title:
The core vector machine-based rapid classification of multi-task concept drift dataset
Author(s):
SHI Yingzhong1; 2;  WANG Shitong1;  DENG Zhaohong1; 3;  HOU Ligong2;  QIAN Dongjie2
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. School of Internet of Things, Wuxi Institute of Technology, Wuxi 214121, China;
3. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122, China
Keywords:
multi-task; large-scale dataset; concept drift; core vector machines; linear time complexity
CLC:
TP181
DOI:
10.11992/tis.201712019
Abstract:
The shared vector chain-supported vector machine (SVC-SVM) can solve multiple concept drift problems as well as related problems, and it shows attractive performance in multi-task concept drift classification. However, in many practical scenarios, the concept drift dataset is usually large, and its high computational cost severely limits the generalization ability of the SVC-SVM. To overcome this shortcoming, a novel classifier termed shared vector chain-core vector machine (SVC-CVM) is proposed for large scale multi-task concept drift dataset, considering the asymptotic linear time complexity of the core vector machines. This classifier has the merit of asymptotic time complexity and inherits the good performance of SVC-SVM in solving multi-task concept drift problems. Furthermore, the effectiveness and rapidness of the proposed method is experimentally confirmed on large-scale multi-task concept drift datasets.
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