A Fuzzy Poisson Naive Bayes Classifier for Epidemiological Purposes

Ronei Marcos de Moraes, Liliane S. Machado

Abstract

Statistical methods have been used to classify data in different areas. In epidemiological studies, some measures follow specific statistical distribution and compatible classifiers can be designed for those cases. Classifiers based on measures that follow Poisson distributions can be found in the scientific literature. Due to uncertainty on epidemiological measures, a fuzzy approach may be interesting and the present work proposes a new classifier named Fuzzy Poisson Naive Bayes (FPNB). The theoretical development is presented as well as results of its application on simulated multidimensional data. A brief comparison with a classical Poisson Naive Bayes classifier and with a Naive Bayes classifier is performed too.

References

  1. Altheneyan, A. S. and Menai, M. E. B. (2014). Naive bayes classifiers for authorship attribution of arabic texts. Journal of King Saud University Computer and Information Sciences, 26(4):473-484.
  2. Bielza, C. and Larranaga, P. (2014). Discrete bayesian network classifiers: A survey. ACM Computing Surveys, 47(1):Article 5.
  3. Bishop, C. (2007). Pattern Recognition and Machine Learning. Springer, Berlin, 1st edition.
  4. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educat. Psyc. Measurement, 20(1):37-46.
  5. Congdon, C. B. (2000). Classification of epidemiological data: a comparison of genetic algorithm and decision tree approaches. In Proceedings of the 2000 Congress on Evolutionary Computation, pages 442-449.
  6. Dubois, D. and Prade, H. (1983). Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms. Fuzzy Sets and Systems, 10(1-3):1520.
  7. Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification. Wiley Interscience, New York, 2nd edition.
  8. Feller, W. (1971). An Introduction to Probability Theory and its Applications. Wiley, 2nd edition.
  9. Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. Pearson, 6th edition.
  10. Kaufmann, M., Meier, A., and Stoffel, K. (2015). Ifcfilter: Membership function generation for inductive fuzzy classification. Expert Systems with Applications, 42:83698379.
  11. Keller, J. M., Gray, M. R., and Givens, J. A. (1985). A fuzzy k-nearest neighbor algoritm. IEEE Trans. Syst. Man and Cybernetics, 15(4):580-585.
  12. Kim, S.-B., Seo, H.-C., and Rim, H.-C. (2003). Poisson naive bayes for text classification with feature weighting. In Proceedings of the Sixth International Workshop on Information Retrieval with Asian Languages, pages 33-40.
  13. Klir, G. J. and Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, 1st edition.
  14. Ma, W. J., Beck, J. M., Latham, P. E., and Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9(11):1432-1438.
  15. Melo, A. C. O., Moraes, R. M., and Machado, L. S. (2003). Gaussian mixture models for supervised classification of remote sensing muliespectral images. Lecture Notes in Computer Science, 2905:440-447.
  16. Moraes, R. M. and Machado, L. S. (2012). Online assessment in medical simulators based on virtual reality using fuzzy gaussian naive bayes. Journal of MultipleValued Logic and Soft Computing, 18(5-6):479-492.
  17. Moraes, R. M. and Machado, L. S. (2014). Psychomotor skills assessment in medical training based on virtual reality using a weighted possibilistic approach. Knowledge Based Systems, 70:97-102.
  18. Moraes, R. M., Rocha, A. V., and Machado, L. S. (2012). Intelligent assessment based on beta regression for realistic training on medical simulators. KnowledgeBased Systems, 32:3-8.
  19. Moraes, R. M., Simas, A. B., Rocha, A. V., and Machado, L. S. (2014). New parameters estimators using emlike algorithm for naive bayes classifier based on beta distributions. In 11th International FLINS Conference on Decision Making and Soft Computing (FLINS2014), pages 155-160, Brazil. World Scientific.
  20. Ogura, H., Amano, H., and Kondo, M. (2014). Classifying documents with poisson mixtures. Transactions on Machine Learning and Artificial Intelligence, 2(4):48-76.
  21. Ramoni, M. and Sebastiani, P. (2001). Robust bayes classifiers. Artificial Intelligence, 125(1-2):209-226.
  22. Richards, J. A. (2013). Remote Sensing Digital Image Analysis: An Introduction. Springer, 5th edition.
  23. Rothman, K. J., Lash, T. L., and Greenland, S. (2012). Modern Epidemiology. Wolters Kluwer, 3rd edition.
  24. Surveillance, H. (2013). Aids e dst. Epidemiological Bulletin: HIV-AIDS - Secretariat of Health Surveillance - Brazilian Ministry of Health, 2(1):1-16.
  25. Surveillance, H. (2014). Monitoramento dos casos de dengue e febre de chikungunya ate a semana epidemiologica 47 de 2014. Epidemiological Bulletin - Secretariat of Health Surveillance - Brazilian Ministry of Health, 45(31):1-7.
  26. Surveillance, H. (2015). Detectar, tratar e curar: desafios e estratgias brasileiras frente tuberculose. Epidemiological Bulletin - Secretariat of Health Surveillance - Brazilian Ministry of Health, 46(9):1-19.
  27. Vadrevu, S. H. R. and Murty, S. U. (2010). A novel tool for classification of epidemiological data of vector-borne diseases. J. Glob Infect Dis., 2(1):35-38.
  28. Viera, A. J. and Garrett, J. M. (2005). Understanding interobserver agreement: The kappa statistic. Family Medicine, 37(5):360-363.
  29. Zadeh, L. A. (1968). Probability measures of fuzzy events. J. Math. Anal. Applic., 10:421-427.
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Paper Citation


in Harvard Style

Moraes R. and S. Machado L. (2015). A Fuzzy Poisson Naive Bayes Classifier for Epidemiological Purposes . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 193-198. DOI: 10.5220/0005642801930198


in Bibtex Style

@conference{fcta15,
author={Ronei Marcos de Moraes and Liliane S. Machado},
title={A Fuzzy Poisson Naive Bayes Classifier for Epidemiological Purposes},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (ECTA 2015)},
year={2015},
pages={193-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005642801930198},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (ECTA 2015)
TI - A Fuzzy Poisson Naive Bayes Classifier for Epidemiological Purposes
SN - 978-989-758-157-1
AU - Moraes R.
AU - S. Machado L.
PY - 2015
SP - 193
EP - 198
DO - 10.5220/0005642801930198