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Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015 pp 101–110Cite as

Semi-supervised Naive Hubness Bayesian k-Nearest Neighbor for Gene Expression Data

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

Abstract

Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of the unlabeled data. Furthermore, gene expression data is high dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers were developed recently, such as Naive Hubness Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN and show in experiments on publicly available gene expression data that the proposed classifier outperforms all its examined competitors.

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Acknowledgments

This research was performed within the framework of the grant of the Hungarian Scientific Research Fund—OTKA 111710 PD. This paper was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

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Authors and Affiliations

  1. BioIntelligence Lab, Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary

    Krisztian Buza

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  1. Krisztian Buza
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Correspondence to Krisztian Buza .

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Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

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Buza, K. (2016). Semi-supervised Naive Hubness Bayesian k-Nearest Neighbor for Gene Expression Data. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26225-3

  • Online ISBN: 978-3-319-26227-7

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