research-article

Multi-Label Punitive kNN with Self-Adjusting Memory for Drifting Data Streams

Authors:

Martha Roseberry,

Bartosz Krawczyk, and

Alberto CanoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 6

Article No.: 60, Pages 1 - 31

https://doi.org/10.1145/3363573

Published: 11 November 2019 Publication History

Abstract

In multi-label learning, data may simultaneously belong to more than one class. When multi-label data arrives as a stream, the challenges associated with multi-label learning are joined by those of data stream mining, including the need for algorithms that are fast and flexible, able to match both the speed and evolving nature of the stream. This article presents a punitive k nearest neighbors algorithm with a self-adjusting memory (MLSAMPkNN) for multi-label, drifting data streams. The memory adjusts in size to contain only the current concept and a novel punitive system identifies and penalizes errant data examples early, removing them from the window. By retaining and using only data that are both current and beneficial, MLSAMPkNN is able to adapt quickly and efficiently to changes within the data stream while still maintaining a low computational complexity. Additionally, the punitive removal mechanism offers increased robustness to various data-level difficulties present in data streams, such as class imbalance and noise. The experimental study compares the proposal to 24 algorithms using 30 real-world and 15 artificial multi-label data streams on six multi-label metrics, evaluation time, and memory consumption. The superior performance of the proposed method is validated through non-parametric statistical analysis, proving both high accuracy and low time complexity. MLSAMPkNN is a versatile classifier, capable of returning excellent performance in diverse stream scenarios.

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Index Terms

Multi-Label Punitive kNN with Self-Adjusting Memory for Drifting Data Streams
1. Computing methodologies
  1. Machine learning

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 6

December 2019

282 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3366748

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2019 ACM.

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Publication History

Published: 11 November 2019

Accepted: 01 August 2019

Revised: 01 April 2019

Received: 01 November 2018

Published in TKDD Volume 13, Issue 6

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2018 VCU Presidential Research Quest Fund and an Amazon AWS Machine Learning Research award

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Cited By

Qin WWang XHu ZWang LLan YHong R(2024)Math Word Problem Generation via Disentangled Memory RetrievalACM Transactions on Knowledge Discovery from Data10.1145/363956918:5(1-21)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3639569
Bakhshi SCan F(2024)Balancing efficiency vs. effectiveness and providing missing label robustness in multi-label stream classificationKnowledge-Based Systems10.1016/j.knosys.2024.111489289(111489)Online publication date: Apr-2024
https://doi.org/10.1016/j.knosys.2024.111489
Liu JHuang KChen CMao J(2024)An oversampling algorithm of multi-label data based on cluster-specific samples and fuzzy rough set theoryComplex & Intelligent Systems10.1007/s40747-024-01498-wOnline publication date: 6-Jun-2024
https://doi.org/10.1007/s40747-024-01498-w
Aguiar GKrawczyk BCano A(2024)A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental frameworkMachine Language10.1007/s10994-023-06353-6113:7(4165-4243)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10994-023-06353-6
Wu HHan MChen ZLi MZhang X(2023)A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data StreamACM Transactions on Knowledge Discovery from Data10.1145/357096017:5(1-21)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3570960
Zhang ZLiu LLi JWu X(2023)Integrating Global and Local Feature Selection for Multi-Label LearningACM Transactions on Knowledge Discovery from Data10.1145/353219017:1(1-37)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3532190
Kumar JShao JKumar RDin SMawuli CYang Q(2023)Online Semi-Supervised Classification on Multilabel Evolving High-Dimensional Text StreamsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.327529853:10(5983-5995)Online publication date: Oct-2023
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Jakob JArtelt AHasenjäger MHammer B(2023)Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data StreamsApplied Artificial Intelligence10.1080/08839514.2023.219884637:1Online publication date: 9-Apr-2023
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