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International Workshop on Machine Learning, Optimization, and Big Data

MOD 2016: Machine Learning, Optimization, and Big Data pp 391–401Cite as

Learning Optimal Decision Lists as a Metaheuristic Search for Diagnosis of Parkinson’s Disease

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10122))

Abstract

Decision Lists are a very general model representation. In learning decision structures from medical datasets one needs a simple understandable model. Such a model should correctly classify unknown cases. One must search for the most general decision structure using the training dataset as input, taking into account both complexity and goodness-of-fit of the underlying model. In this paper, we propose to search the Decision List state space as an optimization problem using a metaheuristic. We implemented the method and carried out experimentation over a well-known Parkinson’s Disease training set. Our results are encouraging when compared to other machine learning references on the same dataset.

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References

  1. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    Article  MathSciNet  Google Scholar 

  2. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org

    Google Scholar 

  3. Rivest, R.L.: Learning Decision Lists (2001). http://people.csail.mit.edu/rivest/Rivest-DecisionLists.pdf

  4. Blum, A.: Rank-r decision trees are a subclass of r-decision lists. Inf. Process. Lett. 42(4), 183–185 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  5. de Campos, L.M., Fernandez-Luna, J.M., Gamez, J.A., Puerta, J.M.: Ant colony optmization for learning Bayesian networks. Int. J. Approx. Reason. 31, 291–311 (2002). Elsevier

    Article  MATH  Google Scholar 

  6. Khan, K., Sahai, A.: A comparison of BA, GA, PSO, BP, and LM for training feed forward neural networks in e-Learning context. Int. J. Intell. Syst. Appl. 7, 23–29 (2012). MECS Press

    Google Scholar 

  7. Bosman, P.A.N., LaPutré, H.: Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case. In: Genetic and Evolutionary Computation Conference, London (2007)

    Google Scholar 

  8. Marchand, M., Sokolova, M.: Learning with decision lists of data-dependent features. J. Mach. Learn. Res. 6, 427–451 (2005)

    MathSciNet  MATH  Google Scholar 

  9. Franco, M.A., Krasnogor, N., Bacardit, J.: Post-processing operators for decision lists. In: GECCO 2012, Philadelphia (2012)

    Google Scholar 

  10. Boström, H.: Covering vs. divide-and-conquer for top-down induction of logic programs. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-1995), Montreal, Canada, pp. 1194–1200 (1995)

    Google Scholar 

  11. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (2000)

    Book  MATH  Google Scholar 

  12. Nock, R., Gascuel, O.: On learning decision committes. In: Proceedings of the Twelfth International Conference on Machine Learning (2016)

    Google Scholar 

  13. Martí, R., Resende, M.G.C., Ribeiro, C.C.: Multi-start methods for combinatorial optimization. Eur. J. Oper. Res. 226(1), 1–8 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  14. Shiek, A., Winkins, S., Fatouros, D.: Metallomic profiling and linkage map analysis of early Parkinson’s disease: a new insight to aluminum marker for the possible diagnosis. PLoS ONE 5, 6 (2010)

    Google Scholar 

  15. Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.A.E., Moroz, I.M.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed. Eng. OnLine, 6–23 (2007)

    Google Scholar 

  16. Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J.: Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans. Biomed. Eng. 56(4), 1015–1022 (2008)

    Article  Google Scholar 

  17. Das, R.: A comparison of multiple classification methods for diagnosis of Parkinson’s Disease. Expert Syst. Appl. 37, 1568–1572 (2010)

    Article  Google Scholar 

  18. Singh, N., Pillay, V., Choonara, Y.E.: Advances in the treatment of Parkinson’s Disease. Prog. Neurobiol. 81, 29–44 (2007)

    Article  Google Scholar 

  19. Astrom, F., Koker, R.: A parallel neural network approach to prediction of Parkinson’s Disease. ESWA 38(10), 12470–12474 (2011)

    Google Scholar 

  20. Sharma, A., Giri, R.N.: Automatic recognition of Parkinson’s Disease via artificial neural network and support vector machine. Int. J. Innov. Technol. Explor. Eng. 4(3), 35–41 (2014)

    Google Scholar 

  21. Avci, D., Dogantekin, A.: An expert diagnosis system for parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson’s Dis. (2016). Article ID 5264743

    Google Scholar 

  22. Shreiner, D., Sellers, G., Kessenich, J.M., Licea-Kane, B.: OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 4.3. Addison-Wesley, Boston (2013)

    Google Scholar 

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Authors and Affiliations

  1. Computer Science Department and Virtual University Institute, Federal University of Ceará, Campus do Pici, Bloco 952, Fortaleza, Ceará, 60455, Brazil

    Fernando de Carvalho Gomes & José Gilvan Rodrigues Maia

Authors
  1. Fernando de Carvalho Gomes
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  2. José Gilvan Rodrigues Maia
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Corresponding author

Correspondence to Fernando de Carvalho Gomes .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Semantic Technology Laboratory, National Research Council (CNR), Catania, Italy

    Piero Conca

  3. Dipartimento di Sociologia e Metodi della Ricerca Sociale, Università di Catania, Catania, Italy

    Giovanni Giuffrida

  4. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Giuseppe Nicosia

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© 2016 Springer International Publishing AG

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de Carvalho Gomes, F., Maia, J.G.R. (2016). Learning Optimal Decision Lists as a Metaheuristic Search for Diagnosis of Parkinson’s Disease. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-51469-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51468-0

  • Online ISBN: 978-3-319-51469-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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