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Pairwise combination of classifiers for ensemble learning on data streams

Authors:

Heitor Murilo Gomes,

Jean Paul Barddal,

Fabrício EnembreckAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 941 - 946

https://doi.org/10.1145/2695664.2695754

Published: 13 April 2015 Publication History

Abstract

This work presents two different voting strategies for ensemble learning on data streams based on pairwise combination of component classifiers. Despite efforts to build a diverse ensemble, there is always some degree of overlap between component classifiers models. Our voting strategies are aimed at using these overlaps to support ensemble prediction. We hypothesize that by combining pairs of classifiers it is possible to alleviate incorrect individual predictions that would otherwise negatively impact the overall ensemble decision. The first strategy, Pairwise Accuracy (PA), combines the shared accuracy estimation of all possible pairs in the ensemble, while the second strategy, Pairwise Patterns (PP), record patterns of pairwise decisions during training and use these patterns during prediction. We present empirical results comparing ensemble classifiers with their original voting methods and our proposed methods in both real and synthetic datasets, with and without concept drifts. Our analysis indicates that pairwise voting is able to enhance overall performance for PP, especially on real datasets, and that PA is useful whenever there are noticeable differences in accuracy estimates among ensemble members, which is common during concept drifts.

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Cited By

Bhatt NThakkar ABhatt NPrajapati P(2023)Challenges and New Opportunities in Diverse Approaches of Big Data Stream AnalyticsProceedings of Third International Conference on Sustainable Expert Systems10.1007/978-981-19-7874-6_31(425-433)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-981-19-7874-6_31
Ji HLiu XTan AWang ZYu B(2020)Research on Equipment Fault Diagnosis Classification Model Based on Integrated Incremental Dynamic Weight CombinationData Science10.1007/978-981-15-7984-4_36(475-489)Online publication date: 20-Aug-2020
https://doi.org/10.1007/978-981-15-7984-4_36
Gomes HBarddal JEnembreck FBifet A(2017)A Survey on Ensemble Learning for Data Stream ClassificationACM Computing Surveys10.1145/305492550:2(1-36)Online publication date: 27-Mar-2017
https://dl.acm.org/doi/10.1145/3054925
Show More Cited By

Index Terms

Pairwise combination of classifiers for ensemble learning on data streams
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

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Published In

cover image ACM Conferences

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

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SAC 2015

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SIGAPP

SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Bhatt NThakkar ABhatt NPrajapati P(2023)Challenges and New Opportunities in Diverse Approaches of Big Data Stream AnalyticsProceedings of Third International Conference on Sustainable Expert Systems10.1007/978-981-19-7874-6_31(425-433)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-981-19-7874-6_31
Ji HLiu XTan AWang ZYu B(2020)Research on Equipment Fault Diagnosis Classification Model Based on Integrated Incremental Dynamic Weight CombinationData Science10.1007/978-981-15-7984-4_36(475-489)Online publication date: 20-Aug-2020
https://doi.org/10.1007/978-981-15-7984-4_36
Gomes HBarddal JEnembreck FBifet A(2017)A Survey on Ensemble Learning for Data Stream ClassificationACM Computing Surveys10.1145/305492550:2(1-36)Online publication date: 27-Mar-2017
https://dl.acm.org/doi/10.1145/3054925
Gomes HOssowski S(2016)Advances in network-based ensemble classifiers for evolving data streamsProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2852021(958-959)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1145/2851613.2852021

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