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Evaluation of Class Binarization and Feature Selection in Tear Film Classification using TOPSIS

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Agents and Artificial Intelligence (ICAART 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 449))

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Abstract

Dry eye syndrome is a prevalent disease which affects a wide range of the population and can be diagnosed through an automatic technique for tear film lipid layer classification. In this setting, class binarization techniques and feature selection are powerful methods to reduce the size of the output and input spaces, respectively. These approaches are expected to reduce the complexity of the multi-class problem of tear film classification. In previous researches, several machine learning algorithms have been tried and only evaluated in terms of accuracy. Up to now, the evaluation of artificial neural networks (ANNs) has not been done in depth. This paper presents a methodology to evaluate the classification performance of ANNs using several measures. For this purpose, the multiple-criteria decision-making method called TOPSIS has been used. The results obtained demonstrate that class binarization and feature selection improves the performance of ANNs on tear film classification.

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Acknowledgements

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 PI10/00578, TIN2009-10748 and TIN2011-25476; and by the Consellería de Industria of the Xunta de Galicia through the research project CN2011/007. Beatriz Remeseiro and Diego Peteiro-Barral acknowledge the support of Xunta de Galicia under Plan I2C Grant Program.

We would also like to thank the Escuela de Óptica y Optometría of the Universidade de Santiago de Compostela for providing us with the annotated image dataset.

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Authors and Affiliations

  1. Departamento de Computación, Universidade da Coruña, Campus de Elviña S/n, 15071, A Coruña, Spain

    Rebeca Méndez, Beatriz Remeseiro, Diego Peteiro-Barral & Manuel G. Penedo

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  1. Rebeca Méndez
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  2. Beatriz Remeseiro
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  3. Diego Peteiro-Barral
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  4. Manuel G. Penedo
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Correspondence to Beatriz Remeseiro .

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  1. INSTICC, Setúbal, Portugal

    Joaquim Filipe

  2. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

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Méndez, R., Remeseiro, B., Peteiro-Barral, D., Penedo, M.G. (2014). Evaluation of Class Binarization and Feature Selection in Tear Film Classification using TOPSIS. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2013. Communications in Computer and Information Science, vol 449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44440-5_11

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  • DOI: https://doi.org/10.1007/978-3-662-44440-5_11

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