Estudio del comportamiento del Algoritmo K* en bases e datos internacionales

  • Yoan Martínez-López Universidad de Camagüey Ignacio Agramonte
  • Julio Madera-Quintana Universidad de Camagüey Ignacio Agramonte
  • Ireimis Leguen de Varona Universidad de Camagüey Ignacio Agramonte
Palabras clave: Clasificación, algoritmo K*, experimental, precisión, datos

Resumen

Este trabajo presenta un estudio experimental del algoritmo K*, el cual se comparó con cinco algoritmos de clasificación de los diez principales algoritmos de minería de datos identificados en la Conferencia Internacional IEEE sobre Minería de Datos (ICDM), los cuales son C4.5, SVM, kNN, Naive Bayes y CART. Los resultados experimentales muestran un rendimiento satisfactorio del algoritmo K* en comparación con estos enfoques.

Biografía del autor/a

Yoan Martínez-López, Universidad de Camagüey Ignacio Agramonte
MSc. Professor, Computer Science Department, Universidad de Camagüey Ignacio Agramonte
Julio Madera-Quintana, Universidad de Camagüey Ignacio Agramonte
Professor of Computer Science Department, Universidad de Camagüey “Ignacio Agramonte”
Ireimis Leguen de Varona, Universidad de Camagüey Ignacio Agramonte
Professor of Computer Science Department, Universidad de Camagüey “Ignacio Agramonte”

Citas

Ian H Witten, Eibe Frank, and Mark A Hall. Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. Elsevier, 2011.

Xindong Wu, Vipin Kumar, J Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J McLachlan, Angus Ng, Bing Liu, S Yu Philip, et al. Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1):1–37, 2008.

David J Hand and Keming Yu. Idiot’s bayes not so stupid after all? International Statistical Review, 69(3):385–398, 2001.

Tang L. Lui H. Refaeilzadeh, P. K-fold Cross-Validation. Arizona State University, 2008.

Thomas Cover and Peter Hart. Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21–27, 1967.

L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Chapman and Hall (Wadsworth and Inc.), 1984.

J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.

J.G. Cleary and L.E. Trigg. K*: An instancebased learner using an entropic distance measure. In Proceedings of the 12th International Conference on Machine Learning, pages 108–114, 1995.

Tejera Hernández, Dayana C. "An Experimental Study of K* Algorithm", Information Engineering and Electronic Business, 2015, 2, 14-19

Uzun, Y. And G. Tezel, Rule Learning With Machine Learning Algorithms And Artificial Neural Networks. Journal of Seljuk University Natural and Applied Science, 2012. 1(2).

Er, E., Identifying At-Risk Students Using Machine Learning Techniques: A Case Study with IS 100. International Journal of Machine Learning and Computing, 2012. 2(4): p. 279

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten. The weka data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1):10-18, 2009.

Janez Demšar. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1–30, 2006.

R. A. Fisher. Statistical methods and scientific inference (2nd edition). MHafner Publishing Co., New York, 1959.

Milton Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200):675–701, 1937.

Milton Friedman. A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11(1):86–92, 1940.

Alcalá-Fdez, J., et al., KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput., 2009. 13: p. 307–318.

Publicado
2016-12-30
Cómo citar
Martínez-López, Y., Madera-Quintana, J., & Leguen de Varona, I. (2016). Estudio del comportamiento del Algoritmo K* en bases e datos internacionales. Revista Politécnica, 12(23), 51-56. Recuperado a partir de https://revistas.elpoli.edu.co/index.php/pol/article/view/898
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