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Real-Time Tear Film Classification Through Cost-Based Feature Selection

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Transactions on Computational Collective Intelligence XX

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9420))

Abstract

Dry eye syndrome is an important public health problem, and can be briefly defined as a symptomatic disease which affects a wide range of population and has a negative impact on their daily activities. In clinical practice, it can be diagnosed by the observation of the tear film lipid layer patterns, and their classification into one of the Guillon categories. However, the time required to extract some features from tear film images prevents the automatic systems to work in real time. In this paper we apply a framework for cost-based feature selection to reduce this high computational time, with the particularity that it takes the cost into account when deciding which features to select. Specifically, three representative filter methods are chosen for the experiments: Correlation-Based Feature Selection (CFS), minimum-Redundancy-Maximum-Relevance (mRMR) and ReliefF. Results with a Support Vector Machine as a classifier showed that the approach is sound, since it allows to reduce considerably the computational time without significantly increasing the classification error.

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Acknowledgments

This research has been partially funded by the Secretaría de Estado de Investigación of the Spanish Government and FEDER funds of the European Union through the research projects TIN2012-37954 and PI14/02161; and by the Consellería de Industria of the Xunta de Galicia through the research projects GPC2013/065 and GRC2014/035.

We would also like to thank the Optometry Service of the University of Santiago de Compostela (Spain) for providing us with the annotated dataset.

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Authors and Affiliations

  1. Departamento de Computación, Universidade da Coruña, A Coruña, Spain

    Verónica Bolón-Canedo, Beatriz Remeseiro, Noelia Sánchez-Maroño & Amparo Alonso-Betanzos

Authors
  1. Verónica Bolón-Canedo
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  2. Beatriz Remeseiro
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  3. Noelia Sánchez-Maroño
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  4. Amparo Alonso-Betanzos
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Corresponding author

Correspondence to Verónica Bolón-Canedo .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Manageme, Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, Australia

    Ryszard Kowalczyk

  3. LERIA - UFR Sciences, Angers Cedex 01, France

    Béatrice Duval

  4. Leiden, Zuid-Holland, Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  5. LERIA - UFR Sciences, Angers Cedex 01, France

    Stephane Loiseau

  6. INSTICC, Polytechnic Institute of Setubal, Setubal, Portugal

    Joaquim Filipe

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Bolón-Canedo, V., Remeseiro, B., Sánchez-Maroño, N., Alonso-Betanzos, A. (2015). Real-Time Tear Film Classification Through Cost-Based Feature Selection. In: Nguyen, N., Kowalczyk, R., Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Transactions on Computational Collective Intelligence XX . Lecture Notes in Computer Science(), vol 9420. Springer, Cham. https://doi.org/10.1007/978-3-319-27543-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-27543-7_4

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