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HSIM: A Supervised Imputation Method for Hierarchical Classification Scenario

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Discovery Science (DS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9956))

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Abstract

The missing value imputation process can be defined as a preprocessing step that fills missing values of attributes in incomplete datasets. Nowadays, the problem of incomplete datasets in the hierarchical classification scenario must be solved using unsupervised missing value imputation methods due to the lack of supervised methods to deal with the hierarchical context. Thus, in this work, we propose and evaluate a supervised missing value imputation method for datasets used in hierarchical classification problems in which the classes are organized into tree structure. Experiments were performed on incomplete datasets to evaluate the effect of the proposed missing value imputation method on classification performance when using a global hierarchical classifier. The results showed that, using the proposed method for dealing with missing attribute values, it provided higher classifier predictive performance than other unsupervised missing value imputation methods.

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Acknowledgements

This research was partially supported by CNPq, FAPEMIG, UFOP, and by individual grants from CAPES.

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Authors and Affiliations

  1. Computer Science Department, Federal University of Ouro Preto, Ouro Preto, Brazil

    Leandro R. Galvão & Luiz H. C. Merschmann

Authors
  1. Leandro R. Galvão
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  2. Luiz H. C. Merschmann
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Corresponding author

Correspondence to Leandro R. Galvão .

Editor information

Editors and Affiliations

  1. Campus Middelhe, M.G.103a, Universiteit Antwerpen Campus Middelhe, M.G.103a, Antwerp, Belgium

    Toon Calders

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Bari, Italy

    Donato Malerba

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Galvão, L.R., Merschmann, L.H.C. (2016). HSIM: A Supervised Imputation Method for Hierarchical Classification Scenario. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-46307-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46306-3

  • Online ISBN: 978-3-319-46307-0

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