Weighted ensemble classification of multi-label data streams

Lulu Wang; Hong Shen; Hui Tian

doi:10.1007/978-3-319-57529-2_43

Weighted ensemble classification of multi-label data streams

Lulu Wang, Hong Shen, Hui Tian

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

21 Citations (Scopus)

Abstract

Many real world applications involve classification of multi-label data streams. However, most existing classification models mostly focused on classifying single-label data streams. Learning in multi-label data stream scenarios is more challenging, as the classification systems should be able to consider several properties, such as large data volumes, label correlations and concept drifts. In this paper, we propose an efficient and effective ensemble model for multi-label stream classification based on ML-KNN (Multi-Label KNN) [31] and propose a balance AdjustWeight function to combine the predictions which can efficiently process high-speed multi-label stream data with concept drifts. The empirical results indicate that our approach achieves a high accuracy and low storage cost, and outperforms the existing methods ML-KNN and SMART [14].

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
Editors	Longbing Cao, Kyuseok Shim, Jae-Gil Lee, Jinho Kim, Yang-Sae Moon, Xuemin Lin
Publisher	Springer Verlag
Pages	551-562
Number of pages	12
ISBN (Print)	9783319575285
DOIs	https://doi.org/10.1007/978-3-319-57529-2_43
Publication status	Published - 2017
Externally published	Yes
Event	21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of Duration: 23 May 2017 → 26 May 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10235 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
Country/Territory	Korea, Republic of
City	Jeju
Period	23/05/17 → 26/05/17

Keywords

Classification
Data stream
Multi-label

Access to Document

10.1007/978-3-319-57529-2_43

Cite this

Wang, L., Shen, H., & Tian, H. (2017). Weighted ensemble classification of multi-label data streams. In L. Cao, K. Shim, J.-G. Lee, J. Kim, Y.-S. Moon, & X. Lin (Eds.), Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings (pp. 551-562). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10235 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-57529-2_43

Wang, Lulu ; Shen, Hong ; Tian, Hui. / Weighted ensemble classification of multi-label data streams. Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. editor / Longbing Cao ; Kyuseok Shim ; Jae-Gil Lee ; Jinho Kim ; Yang-Sae Moon ; Xuemin Lin. Springer Verlag, 2017. pp. 551-562 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Weighted ensemble classification of multi-label data streams",

abstract = "Many real world applications involve classification of multi-label data streams. However, most existing classification models mostly focused on classifying single-label data streams. Learning in multi-label data stream scenarios is more challenging, as the classification systems should be able to consider several properties, such as large data volumes, label correlations and concept drifts. In this paper, we propose an efficient and effective ensemble model for multi-label stream classification based on ML-KNN (Multi-Label KNN) [31] and propose a balance AdjustWeight function to combine the predictions which can efficiently process high-speed multi-label stream data with concept drifts. The empirical results indicate that our approach achieves a high accuracy and low storage cost, and outperforms the existing methods ML-KNN and SMART [14].",

keywords = "Classification, Data stream, Multi-label",

author = "Lulu Wang and Hong Shen and Hui Tian",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 ; Conference date: 23-05-2017 Through 26-05-2017",

year = "2017",

doi = "10.1007/978-3-319-57529-2_43",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Longbing Cao and Kyuseok Shim and Jae-Gil Lee and Jinho Kim and Yang-Sae Moon and Xuemin Lin",

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Wang, L, Shen, H & Tian, H 2017, Weighted ensemble classification of multi-label data streams. in L Cao, K Shim, J-G Lee, J Kim, Y-S Moon & X Lin (eds), Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10235 LNAI, Springer Verlag, pp. 551-562, 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017, Jeju, Korea, Republic of, 23/05/17. https://doi.org/10.1007/978-3-319-57529-2_43

Weighted ensemble classification of multi-label data streams. / Wang, Lulu; Shen, Hong; Tian, Hui.
Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. ed. / Longbing Cao; Kyuseok Shim; Jae-Gil Lee; Jinho Kim; Yang-Sae Moon; Xuemin Lin. Springer Verlag, 2017. p. 551-562 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10235 LNAI).

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

TY - GEN

T1 - Weighted ensemble classification of multi-label data streams

AU - Wang, Lulu

AU - Shen, Hong

AU - Tian, Hui

PY - 2017

Y1 - 2017

N2 - Many real world applications involve classification of multi-label data streams. However, most existing classification models mostly focused on classifying single-label data streams. Learning in multi-label data stream scenarios is more challenging, as the classification systems should be able to consider several properties, such as large data volumes, label correlations and concept drifts. In this paper, we propose an efficient and effective ensemble model for multi-label stream classification based on ML-KNN (Multi-Label KNN) [31] and propose a balance AdjustWeight function to combine the predictions which can efficiently process high-speed multi-label stream data with concept drifts. The empirical results indicate that our approach achieves a high accuracy and low storage cost, and outperforms the existing methods ML-KNN and SMART [14].

AB - Many real world applications involve classification of multi-label data streams. However, most existing classification models mostly focused on classifying single-label data streams. Learning in multi-label data stream scenarios is more challenging, as the classification systems should be able to consider several properties, such as large data volumes, label correlations and concept drifts. In this paper, we propose an efficient and effective ensemble model for multi-label stream classification based on ML-KNN (Multi-Label KNN) [31] and propose a balance AdjustWeight function to combine the predictions which can efficiently process high-speed multi-label stream data with concept drifts. The empirical results indicate that our approach achieves a high accuracy and low storage cost, and outperforms the existing methods ML-KNN and SMART [14].

KW - Classification

KW - Data stream

KW - Multi-label

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DO - 10.1007/978-3-319-57529-2_43

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AN - SCOPUS:85018399844

SN - 9783319575285

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 551

EP - 562

BT - Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings

A2 - Cao, Longbing

A2 - Shim, Kyuseok

A2 - Lee, Jae-Gil

A2 - Kim, Jinho

A2 - Moon, Yang-Sae

A2 - Lin, Xuemin

PB - Springer Verlag

T2 - 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017

Y2 - 23 May 2017 through 26 May 2017

ER -

Wang L, Shen H, Tian H. Weighted ensemble classification of multi-label data streams. In Cao L, Shim K, Lee JG, Kim J, Moon YS, Lin X, editors, Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings. Springer Verlag. 2017. p. 551-562. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-57529-2_43

Weighted ensemble classification of multi-label data streams

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Keywords

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