Combining instance selection and self-training to improve data stream quantification View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

André G. Maletzke, Denis M. dos Reis, Gustavo E. A. P. A. Batista

ABSTRACT

In the last years, learning from data streams has attracted the attention of researchers and practitioners due to its large number of applications. These applications have motivated the research community to propose a significant amount of methods to solve problems in diverse tasks, more prominently in classification, clustering, and anomaly detection. However, a relevant task known as quantification has remained mostly unexplored. The quantification goal is to provide an estimate of the class prevalence in an unlabeled set. Recently, we proposed the SQSI algorithm to quantify data streams with concept drifts. SQSI uses a statistical test to identify concept drifts and retrain the classifiers. However, the retraining involves requiring the labels for all newly arrived instances. In this paper, we extend SQSI algorithm by exploring instance selection techniques allied to semi-supervised learning. The idea is to request the classes of a smaller subset of recent examples. Our experiments demonstrate that SQSI’s extension significantly reduces the dependency on actual labels while maintaining or improving the quantification accuracy. More... »

PAGES

12

References to SciGraph publications

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  • 1936-07. Humidity and Insect Metabolism in NATURE
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  • 2015-12. A survey on data stream clustering and classification in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2016-12. From classification to quantification in tweet sentiment analysis in SOCIAL NETWORK ANALYSIS AND MINING
  • 2004. Learning with Drift Detection in ADVANCES IN ARTIFICIAL INTELLIGENCE – SBIA 2004
  • 2014-06. Event labeling combining ensemble detectors and background knowledge in PROGRESS IN ARTIFICIAL INTELLIGENCE
  • 2014-09. Flying Insect Classification with Inexpensive Sensors in JOURNAL OF INSECT BEHAVIOR
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    http://scigraph.springernature.com/pub.10.1186/s13173-018-0076-0

    DOI

    http://dx.doi.org/10.1186/s13173-018-0076-0

    DIMENSIONS

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