Next Article in Journal
Improving Intrusion Detection Model Prediction by Threshold Adaptation
Previous Article in Journal
DGA CapsNet: 1D Application of Capsule Networks to DGA Detection
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle

Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift

1,2,3,*, 1 and 1
1
School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
2
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
3
Henan Key Lab of Analysis and Applications of Education Big Data, Xinyang Normal University, Xinyang 464000, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(5), 158; https://doi.org/10.3390/info10050158
Received: 20 February 2019 / Revised: 14 April 2019 / Accepted: 18 April 2019 / Published: 28 April 2019
  |  
PDF [1610 KB, uploaded 28 April 2019]
  |     |  

Abstract

Most existing multi-label data streams classification methods focus on extending single-label streams classification approaches to multi-label cases, without considering the special characteristics of multi-label stream data, such as label dependency, concept drift, and recurrent concepts. Motivated by these challenges, we devise an efficient ensemble paradigm for multi-label data streams classification. The algorithm deploys a novel change detection based on Jensen–Shannon divergence to identify different kinds of concept drift in data streams. Moreover, our method tries to consider label dependency by pruning away infrequent label combinations to enhance classification performance. Empirical results on both synthetic and real-world datasets have demonstrated its effectiveness. View Full-Text
Keywords: data streams; multi-label; concept drift; ensemble classification; label dependency data streams; multi-label; concept drift; ensemble classification; label dependency
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Sun, Y.; Shao, H.; Wang, S. Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift. Information 2019, 10, 158.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top