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Online Fairness-Aware Learning Under Class Imbalance

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

  • Vasileios Iosifidis
  • Eirini Ntoutsi

Research Organisations

Details

Original languageEnglish
Title of host publicationDiscovery Science
Subtitle of host publication23rd International Conference, DS 2020, Proceedings
EditorsAnnalisa Appice, Grigorios Tsoumakas, Yannis Manolopoulos, Stan Matwin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages159-174
Number of pages16
ISBN (electronic)9783030615277
ISBN (print)9783030615260
Publication statusPublished - 15 Oct 2020
Event23rd International Conference on Discovery Science, DS 2020 - Thessaloniki, Greece
Duration: 19 Oct 202021 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Abstract

Data-driven algorithms are employed in many applications, in which data become available in a sequential order, forcing the update of the model with new instances. In such dynamic environments, in which the underlying data distributions might evolve with time, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class distribution skewness. As a result, such methods mitigate discrimination by “rejecting” minority instances at large due to their inability to effectively learn all classes. In this work, we propose, an online fairness-aware approach that maintains a valid and fair classifier over a stream is an online boosting approach that changes the training distribution in an online fashion based on both stream imbalance and discriminatory behavior of the model evaluated over the historical stream. Our experiments show that such long-term consideration of class-imbalance and fairness are beneficial for maintaining models that exhibit good predictive- and fairness-related performance.

Keywords

    Class-imbalance, Data streams, Fairness-aware classification

ASJC Scopus subject areas

Cite this

Online Fairness-Aware Learning Under Class Imbalance. / Iosifidis, Vasileios; Ntoutsi, Eirini.
Discovery Science : 23rd International Conference, DS 2020, Proceedings. ed. / Annalisa Appice; Grigorios Tsoumakas; Yannis Manolopoulos; Stan Matwin. Springer Science and Business Media Deutschland GmbH, 2020. p. 159-174 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Iosifidis, V & Ntoutsi, E 2020, Online Fairness-Aware Learning Under Class Imbalance. in A Appice, G Tsoumakas, Y Manolopoulos & S Matwin (eds), Discovery Science : 23rd International Conference, DS 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH, pp. 159-174, 23rd International Conference on Discovery Science, DS 2020, Thessaloniki, Greece, 19 Oct 2020. https://doi.org/10.1007/978-3-030-61527-7_11
Iosifidis, V., & Ntoutsi, E. (2020). Online Fairness-Aware Learning Under Class Imbalance. In A. Appice, G. Tsoumakas, Y. Manolopoulos, & S. Matwin (Eds.), Discovery Science : 23rd International Conference, DS 2020, Proceedings (pp. 159-174). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61527-7_11
Iosifidis V, Ntoutsi E. Online Fairness-Aware Learning Under Class Imbalance. In Appice A, Tsoumakas G, Manolopoulos Y, Matwin S, editors, Discovery Science : 23rd International Conference, DS 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 159-174. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-61527-7_11
Iosifidis, Vasileios ; Ntoutsi, Eirini. / Online Fairness-Aware Learning Under Class Imbalance. Discovery Science : 23rd International Conference, DS 2020, Proceedings. editor / Annalisa Appice ; Grigorios Tsoumakas ; Yannis Manolopoulos ; Stan Matwin. Springer Science and Business Media Deutschland GmbH, 2020. pp. 159-174 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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