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Quantification and semi-supervised classification methods for handling changes in class distribution

Authors:

Jack Chongjie Xue,

Gary M. WeissAuthors Info & Claims

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 897 - 906

https://doi.org/10.1145/1557019.1557117

Published: 28 June 2009 Publication History

Abstract

In realistic settings the prevalence of a class may change after a classifier is induced and this will degrade the performance of the classifier. Further complicating this scenario is the fact that labeled data is often scarce and expensive. In this paper we address the problem where the class distribution changes and only unlabeled examples are available from the new distribution. We design and evaluate a number of methods for coping with this problem and compare the performance of these methods. Our quantification-based methods estimate the class distribution of the unlabeled data from the changed distribution and adjust the original classifier accordingly, while our semi-supervised methods build a new classifier using the examples from the new (unlabeled) distribution which are supplemented with predicted class values. We also introduce a hybrid method that utilizes both quantification and semi-supervised learning. All methods are evaluated using accuracy and F-measure on a set of benchmark data sets. Our results demonstrate that our methods yield substantial improvements in accuracy and F-measure.

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Cited By

Donyavi ZSerapião ABatista G(2024)MC-SQ and MC-MQ: Ensembles for Multi-Class QuantificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337201136:8(4007-4019)Online publication date: Aug-2024
https://doi.org/10.1109/TKDE.2024.3372011
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)The Road AheadLearning to Quantify10.1007/978-3-031-20467-8_7(121-123)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_7
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)The Quantification LandscapeLearning to Quantify10.1007/978-3-031-20467-8_6(103-120)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_6
Show More Cited By

Index Terms

Quantification and semi-supervised classification methods for handling changes in class distribution
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning

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Published In

cover image ACM Conferences

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

June 2009

1426 pages

ISBN:9781605584959

DOI:10.1145/1557019

General Chairs:
John Elder
Elder Research, Inc., USA
,
Françoise Soulié Fogelman
KXEN, France
,
Program Chairs:
Peter Flach
University of Bristol, UK
,
Mohammed Zaki
RPI, USA

Copyright © 2009 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 ACM 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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Published: 28 June 2009

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KDD09

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28 - July 1, 2009

Paris, France

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Donyavi ZSerapião ABatista G(2024)MC-SQ and MC-MQ: Ensembles for Multi-Class QuantificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337201136:8(4007-4019)Online publication date: Aug-2024
https://doi.org/10.1109/TKDE.2024.3372011
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)The Road AheadLearning to Quantify10.1007/978-3-031-20467-8_7(121-123)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_7
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)The Quantification LandscapeLearning to Quantify10.1007/978-3-031-20467-8_6(103-120)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_6
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)Advanced TopicsLearning to Quantify10.1007/978-3-031-20467-8_5(87-101)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_5
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)Methods for Learning to QuantifyLearning to Quantify10.1007/978-3-031-20467-8_4(55-85)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_4
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)Evaluation of Quantification AlgorithmsLearning to Quantify10.1007/978-3-031-20467-8_3(33-54)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_3
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)Applications of QuantificationLearning to Quantify10.1007/978-3-031-20467-8_2(19-31)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_2
Esuli AFabris AMoreo ASebastiani FEsuli AFabris AMoreo ASebastiani F(2022)The Case for QuantificationLearning to Quantify10.1007/978-3-031-20467-8_1(1-17)Online publication date: 29-Dec-2022
https://doi.org/10.1007/978-3-031-20467-8_1
Daughton APaul M(2021)A bootstrapping approach to social media quantificationSocial Network Analysis and Mining10.1007/s13278-021-00760-011:1Online publication date: 9-Aug-2021
https://doi.org/10.1007/s13278-021-00760-0
Sun ZTang JQiao JCui C(2020)Review of Concept Drift Detection Method for Industrial Process Modeling2020 39th Chinese Control Conference (CCC)10.23919/CCC50068.2020.9189106(5754-5759)Online publication date: Jul-2020
https://doi.org/10.23919/CCC50068.2020.9189106
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