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Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features

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Discovery Science (DS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14276))

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Abstract

Replacing missing features in data streams is an important task in order to enable many machine learning algorithms that require feature-complete instances for training and prediction. Two popular methods for dealing with missing features are imputation and active feature acquisition, where in the former missing values are approximated, whereas in the latter, missing features are provided by an expert for a cost and within a limited budget. In this work, we present a hybridized approach, where we employ an active feature acquisition method in the first stage to pick candidate features on which we would require a costly expert and then check in a second stage how well we could impute these candidate features. If the imputation is expected to be of a certain quality, we skip the purchase and impute instead. We provide a framework for such a scenario and used it to run extensive experiments. Our results on 6 data sets show that our proposed method can achieve a similar classification performance while spending 1% to 27% less budget.

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Notes

  1. 1.

    https://github.com/Buettner-Maik/caafa-stream.

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Authors and Affiliations

  1. Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

    Maik Büttner, Christian Beyer & Myra Spiliopoulou

Authors
  1. Maik Büttner
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  2. Christian Beyer
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  3. Myra Spiliopoulou
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Corresponding author

Correspondence to Maik Büttner .

Editor information

Editors and Affiliations

  1. Waikato University, Hamilton, New Zealand

    Albert Bifet

  2. Aeronautics Institute of Technology, São José dos Campos, Brazil

    Ana Carolina Lorena

  3. University of Porto, Porto, Portugal

    Rita P. Ribeiro

  4. University of Porto, Porto, Portugal

    João Gama

  5. University of Coimbra, Coimbra, Portugal

    Pedro H. Abreu

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Büttner, M., Beyer, C., Spiliopoulou, M. (2023). Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features. In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham. https://doi.org/10.1007/978-3-031-45275-8_21

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  • DOI: https://doi.org/10.1007/978-3-031-45275-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45274-1

  • Online ISBN: 978-3-031-45275-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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