A novel data stream learning approach to tackle one-sided label noise from verification latency

Liyan Song, Shuxian Li, Leandro Minku*, Xin Yao*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Many real-world data stream applications suffer from verification latency, where the labels of the training examples arrive with a delay. In binary classification problems, the labeling process frequently involves waiting for a pre-determined period of time to observe an event that assigns the example to a given class. Once this time passes, if such labeling event does not occur, the example is labeled as belonging to the other class. For example, in software defect prediction, one may wait to see if a defect is associated to a software change implemented by a developer, producing a defect-inducing training example. If no defect is found during the waiting time, the training example is labeled as clean. Such verification latency inherently causes label noise associated to insufficient waiting time. For example, a defect may be observed only after the pre-defined waiting time has passed, resulting in a noisy example of the clean class. Due to the nature of the waiting time, such noise is frequently onesided, meaning that it only occurs to examples of one of the classes. However, no existing work tackles label noise associated to verification latency. This paper proposes a novel data stream learning approach that estimates the confidence in the labels assigned to the training examples and uses this to improve predictive performance in problems with one-sided label noise. Our experiments with 14 real-world datasets from the domain of software defect prediction demonstrate the effectiveness of the proposed approach compared to existing ones.
Original languageEnglish
Title of host publication2022 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-7281-8671-9
ISBN (Print)978-1-6654-9526-4
DOIs
Publication statusE-pub ahead of print - 30 Sept 2022
Event2022 International Joint Conference on Neural Networks (IJCNN) - University of Padua, Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

NameInternational Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

Conference2022 International Joint Conference on Neural Networks (IJCNN)
Abbreviated titleIJCNN 2022
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22

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