Multi-criteria Evaluation of Class Binarization and Feature Selection in Tear Film Lipid Layer Classification

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


Dry eye is an increasingly popular syndrome in modern society which can be diagnosed through an automatic technique for tear film lipid layer classification. Previous studies related to this multi-class problem lack of analysis focus on class binarization techniques, feature selection and artificial neural networks. Also, all of them just use the accuracy of the machine learning algorithms as performance measure. This paper presents a methodology to evaluate different performance measures over these unexplored areas using the multiple criteria decision making method called TOPSIS. The results obtained demonstrate the effectiveness of the methodology proposed in this research.


  1. Bron, A., Tiffany, J., Gouveia, S., Yokoi, N., and Voon, L. (2004). Functional aspects of the tear film lipid layer. Experimental Eye Research, 78(3):347-360.
  2. Calvo, D., Mosquera, A., Penas, M., García-Resúa, C., and Remeseiro, B. (2010). Color Texture Analysis for Tear Film Classification: A Preliminary Study. In Lecture Notes in Computer Science: International Conference on Image Analysis and Recognition (ICIAR), volume 6112, pages 388-397.
  3. Crammer, K. and Singer, Y. (2002). On the learnability and design of output codes for multiclass problems. Machine Learning, 47(2):201-233.
  4. Dash, M. and Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1- 2):155-176.
  5. Dietterich, T. and Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263- 286.
  6. E.Wolff (1954). Anatomy of the eye and orbit (4th edition). H. K. Lewis and Co., London.
  7. Fernandez Caballero, J., Martínez, F., Hervás, C., and Gutiérrez, P. (2010). Sensitivity versus accuracy in multiclass problems using memetic pareto evolutionary neural networks. Neural Networks, IEEE Transactions on, 21(5):750-770.
  8. Furnkranz, J. (2002a). Pairwise classification as an ensemble technique. Machine Learning: ECML 2002, pages 9-38.
  9. Furnkranz, J. (2002b). Round robin classification. The Journal of Machine Learning Research, 2:721-747.
  10. García-Resúa, C., Giráldez-Fernández, M., Penedo, M., Calvo, D., Penas, M., and Yebra-Pimentel, E. (2012). New software application for clarifying tear film lipid layer patterns. Cornea.
  11. Goto, E., Yagi, Y., Kaido, M., Matsumoto, Y., Konomi, K., and Tsubota, K. (2003). Improved functional visual acuity after punctual occlusion in dry eye patients. Am J Ophthalmol, 135(5):704-705.
  12. Guillon, J. (1998). Non-invasive tearscope plus routine for contact lens fitting. Cont Lens Anterior Eye, 21 Suppl 1.
  13. Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. (2006). Feature Extraction: Foundations and Applications. Springer Verlag.
  14. Hall, M. (1999). Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato.
  15. Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6):610-621.
  16. Hecht-Nielsen, R. (1990). Wesley.
  17. Hwang, C. and Yoon, K. (1981). Multiple attribute decision making: methods and applications: a state-of-the-art survey, volume 13. Springer-Verlag New York.
  18. King-Smith, P., Fink, B., and Fogt, N. (1999). Three interferometric methods for measuring the thickness of layers of the tear film. Optom Vis Sci, 76:19-32.
  19. Korb, D. (2002). The Tear Film: Structure, Function and Clinical Examination. Butterworth-Heinemann.
  20. Kou, G., Lu, Y., Peng, Y., and Shi, Y. (2012). Evaluation of Classification Algorithms using MCDM and Rank Correlation. International Journal of Information Technology & Decision Making (IJITDM), 11(01):197-225.
  21. Lemp, M., Baudouin, C., Baum, J., Dogru, M., Foulks, G., Kinoshita, S., Laibson, P., McCulley, J., Murube, J., Pfugfelder, S., Rolando, M., and Toda, I. (2007). The definition and classification of dry eye disease: Report of the definition and classification subcommittee of the internation dry eye workshop (2007). Ocular Surface, 5(2):75-92.
  22. Loughrey, J. and Cunningham, P. (2005). Overfitting in wrapper-based feature subset selection: The harder you try the worse it gets. Research and Development in Intelligent Systems XXI, pages 33-43.
  23. McLaren, K. (1976). The development of the CIE 1976 (L*a*b) uniform colour-space and colour-difference formula. Journal of the Society of Dyers and Colourists, 92(9):338-341.
  24. Nichols, K., Nichols, J., and Mitchell, G. (2004). The lack of association between signs and symptons in patients with dry eye disease. Cornea, 23(8):762-770.
  25. Olson, D. (2004). Comparison of weights in TOPSIS models. Mathematical and Computer Modelling, 40(7- 8):721-727.
  26. Opricovic, S. and Tzeng, G. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2):445-455.
  27. Press, W., Flannery, B., Teukolsky, S., Vetterling, W., et al. (1986). Numerical recipes, volume 547. Cambridge Univ Press.
  28. Ramos, L., Penas, M., Remeseiro, B., Mosquera, A., Barreira, N., and Yebra-Pimentel, E. (2011). Texture and color analysis for the automatic classification of the eye lipid layer. In LNCS: Advances in Computational Intelligence (International Work Conference on Artificial Neural Networks-IWANN 2011), volume 6692, pages 66-73.
  29. Remeseiro, B. (2012). VOPTICAL I1, VARPA optical dataset annotated by optometrists from the Faculty of Optics and Optometry, University of Santiago de Compostela (Spain).
  30. I1.html, october 2012.
  31. Remeseiro, B., Penas, M., Mosquera, A., Novo, J., Penedo, M., and Yebra-Pimentel, E. (2012). Statistical comparison of classifiers applied to the interferential tear film lipid layer automatic classification. Computational and Mathematical Methods in Medicine, 2012.
  32. Remeseiro, B., Ramos, L., Penas, M., Martínez, E., Penedo, M., and Mosquera, A. (2011). Colour texture analysis for classifying the tear film lipid layer: a comparative study. In International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 268-273, Noosa, Australia.
  33. Rifkin, R. and Klautau, A. (2004). In defense of one-vsall classification. The Journal of Machine Learning Research, 5:101-141.
  34. Rolando, M., Iester, M., Marcrí, A., and Calabria, G. (1998). Low spatial-contrast sensitivity in dry eyes. Cornea, 17(4):376-379.
  35. Rolando, M., Refojo, M., and Kenyon, K. (1983). Increased tear evaporation in eyes with keratoconjunctivitis sicca. Arch Ophthalmol, 101(4):557-558.
  36. Zhao, Z. and Liu, H. (2007). Searching for interacting features. In Proceedings of the 20th international joint conference on Artifical intelligence, pages 1156- 1161. Morgan Kaufmann Publishers Inc.

Paper Citation

in Harvard Style

Méndez R., Remeseiro B., Peteiro-Barral D. and G. Penedo M. (2013). Multi-criteria Evaluation of Class Binarization and Feature Selection in Tear Film Lipid Layer Classification . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 62-70. DOI: 10.5220/0004224300620070

in Bibtex Style

author={Rebeca Méndez and Beatriz Remeseiro and Diego Peteiro-Barral and Manuel G. Penedo},
title={Multi-criteria Evaluation of Class Binarization and Feature Selection in Tear Film Lipid Layer Classification},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Multi-criteria Evaluation of Class Binarization and Feature Selection in Tear Film Lipid Layer Classification
SN - 978-989-8565-39-6
AU - Méndez R.
AU - Remeseiro B.
AU - Peteiro-Barral D.
AU - G. Penedo M.
PY - 2013
SP - 62
EP - 70
DO - 10.5220/0004224300620070