A new ensemble method for multi-label data stream classification in non-stationary environment

Ge Song; Yunming Ye

doi:10.1109/IJCNN.2014.6889846

A new ensemble method for multi-label data stream classification in non-stationary environment

Ge Song^*
, Yunming Ye

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Most existing approaches for the data stream classification focus on single-label data in non-stationary environment. In these methods, each instance can only be tagged with one label. However, in many realistic applications, each instance should be tagged with more than one label. To address the challenge of classifying multi-label stream in evolving environment, we propose a novel Multi-Label Dynamic Ensemble (MLDE) approach. The proposed MLDE integrates a number of Multi-Label Cluster-based Classifiers (MLCCs). MLDE includes an adaptive ensemble method and an ensemble voting method with two important weights, subset accuracy weight and similarity weight. Experimental results reveal that MLDE achieves better performance than state-of-the-art multi-label stream classification algorithms.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1776-1783
Number of pages	8
ISBN (Electronic)	9781479914845
DOIs	https://doi.org/10.1109/IJCNN.2014.6889846
State	Published - 3 Sep 2014
Externally published	Yes
Event	2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14

Keywords

Concept drift
Data stream classification
Ensemble learning
Multi-label classification

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10.1109/IJCNN.2014.6889846

Cite this

Song, G., & Ye, Y. (2014). A new ensemble method for multi-label data stream classification in non-stationary environment. In Proceedings of the International Joint Conference on Neural Networks (pp. 1776-1783). Article 6889846 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889846

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title = "A new ensemble method for multi-label data stream classification in non-stationary environment",

abstract = "Most existing approaches for the data stream classification focus on single-label data in non-stationary environment. In these methods, each instance can only be tagged with one label. However, in many realistic applications, each instance should be tagged with more than one label. To address the challenge of classifying multi-label stream in evolving environment, we propose a novel Multi-Label Dynamic Ensemble (MLDE) approach. The proposed MLDE integrates a number of Multi-Label Cluster-based Classifiers (MLCCs). MLDE includes an adaptive ensemble method and an ensemble voting method with two important weights, subset accuracy weight and similarity weight. Experimental results reveal that MLDE achieves better performance than state-of-the-art multi-label stream classification algorithms.",

keywords = "Concept drift, Data stream classification, Ensemble learning, Multi-label classification",

author = "Ge Song and Yunming Ye",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

year = "2014",

month = sep,

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doi = "10.1109/IJCNN.2014.6889846",

language = "英语",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of the International Joint Conference on Neural Networks",

address = "美国",

}

Song, G & Ye, Y 2014, A new ensemble method for multi-label data stream classification in non-stationary environment. in Proceedings of the International Joint Conference on Neural Networks., 6889846, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., pp. 1776-1783, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/IJCNN.2014.6889846

A new ensemble method for multi-label data stream classification in non-stationary environment. / Song, Ge; Ye, Yunming.
Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1776-1783 6889846 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Most existing approaches for the data stream classification focus on single-label data in non-stationary environment. In these methods, each instance can only be tagged with one label. However, in many realistic applications, each instance should be tagged with more than one label. To address the challenge of classifying multi-label stream in evolving environment, we propose a novel Multi-Label Dynamic Ensemble (MLDE) approach. The proposed MLDE integrates a number of Multi-Label Cluster-based Classifiers (MLCCs). MLDE includes an adaptive ensemble method and an ensemble voting method with two important weights, subset accuracy weight and similarity weight. Experimental results reveal that MLDE achieves better performance than state-of-the-art multi-label stream classification algorithms.

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KW - Ensemble learning

KW - Multi-label classification

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Song G, Ye Y. A new ensemble method for multi-label data stream classification in non-stationary environment. In Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1776-1783. 6889846. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2014.6889846

A new ensemble method for multi-label data stream classification in non-stationary environment

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Keywords

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