Algorithms for Attribute Selection and Knowledge Discovery

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 731)

Abstract

The features relevant selection is a task performed prior to the data mining and can be seen as one of the most important problems to solve in the data preprocessing stage an in the machine learning. With the feature selection is mainly intended to improve predictive or descriptive performance of models and implement faster and less expensive algorithms. In this paper an analysis about feature selection methods is made emphasizing on decision trees, entropy measure for ranking features, and estimation of distribution algorithms. Finally, we show the result analysis of execute the three algorithms.

Keywords

Features selection Complexity Decision trees Entropy Estimation of distribution algorithms Machine learning Data mining 

References

  1. 1.
    Larose, D.: Data Mining: Methods and Models, pp. 1–3. Wiley-Interscience, USA (2006)Google Scholar
  2. 2.
    Pyle, D.: Data Preparation for Data Mining, pp. 15–19. Morgan Kaufmann Publisher, USA (1999)Google Scholar
  3. 3.
    Bradley, P., Mangasarian, O.: Feature selection via concave minimization and support vector machine. J. Mach. Learn. ICML, 82–90 (1998). USAGoogle Scholar
  4. 4.
    Lei, Y., Huan, L.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004). USAGoogle Scholar
  5. 5.
    Guyon, I., Elissee, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003). USAGoogle Scholar
  6. 6.
    Dash, M., Liu, H.: Feature selection for classification. J. Intell. Data Anal. 1(3), 131–156 (1996). USAGoogle Scholar
  7. 7.
    Liu, H., Lei, Y.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005). USACrossRefGoogle Scholar
  8. 8.
    Kohavi, R., John, G.: Wrappers for feature subset selection. Artif. Intell. 97(12), 273–324 (1997). USACrossRefGoogle Scholar
  9. 9.
    Jennifer, G.: Feature selection for unsupervised learning. J. Mach. Learn. Res. 5, 845–889 (2004). USAGoogle Scholar
  10. 10.
    Das, S.: Filters, wrappers and a boosting-based hybrid for feature selection. In: Proceedings of the 18th International Conference on Machine Learning, USA, pp. 74–81 (2001)Google Scholar
  11. 11.
    Cardie, (2001): Using decision trees to improve case-based learning. In: Utgo, P. (ed.) Proceedings of the 10th International Conference on Machine Learning, USA, pp. 25–32 (1993)Google Scholar
  12. 12.
    Mucciardi, A., Gose, E.: A comparison of seven techniques for choosing subsets of pattern recognition. IEEE Trans. Comput. 20, 1023–1031 (1971). USACrossRefGoogle Scholar
  13. 13.
    Ruiz, R., Riquelme, J., Aguilar-Ruiz, J.: Projection-based measure for efficient feature selection. J. Intell. Fuzzy Syst. 12, 175–183 (2003). USAGoogle Scholar
  14. 14.
    Pérez, I., Sánchez, R.: Adaptación del método de reducción no lineal LLE para la selección de atributos en WEKA. In: III Conferencia Internacional en Ciencias Computacionales e Informáticas, Cuba, pp. 1–7 (2016)Google Scholar
  15. 15.
    Winston, P.: Inteligencia Artificial, pp. 455–460. Addison Wesley, USA (1994)Google Scholar
  16. 16.
    Chourasia, S.: Survey paper on improved methods of ID3 decision tree classification. Int. J. Sci. Res. Pub. 3, 1–4 (2013). USAGoogle Scholar
  17. 17.
    Rodríguez, J.: Fundamentos de minería de datos. Fondo de publicaciones de la Universidad Distrital Francisco José de Caldas, Colombia, pp. 63–64 (2010)Google Scholar
  18. 18.
    Changala, R., Gummadi, A., Yedukondalu, G., Raju, U.N.P.G.: Classification by decision tree induction algorithm to learn decision trees from the class-labeled training tuples. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(4), 427–434 (2012). USAGoogle Scholar
  19. 19.
    Michell, T.: Machine Learning, pp. 50–56. McGraw Hill, USA (1997)Google Scholar
  20. 20.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, vol. 3, pp. 331–336. McGraw Hill, USA (2012)Google Scholar
  21. 21.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, pp. 531–540. Prentice Hall, USA (2012)Google Scholar
  22. 22.
    Kantardzic, M.: Data Mining: Concepts, Models, Methods and Algorithms, pp. 46–48. IEEE Press Wiley-Interscience, USA (2003)Google Scholar
  23. 23.
    Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining, vol. 2, pp. 30–35. Kluwer Academic Publisher, USA (2000)Google Scholar
  24. 24.
    Liu, H., Motoda, H.: Feature Extraction, Construction and Selection. A Data Mining Perspective, pp. 20–28. Kluwer Academic Publisher, USA (2000)Google Scholar
  25. 25.
    Larrañaga, P., Lozano, J.: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation, pp. 1–2. Kluwer Academic Publishers, USA (2002)Google Scholar
  26. 26.
    Pelikan, M., Sastry, K.: Initial-population bias in the univariate estimation of distribution algorithm. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, USA, vol. 11, pp. 429–436 (2002)Google Scholar
  27. 27.
    Pérez, R., Hernández, A.: Un algoritmo de estimación de distribuciones para el problema de secuencia-miento en configuración jobshop, vol. 1, pp. 1–4. Communication Del CIMAT, Mexico (2015)Google Scholar
  28. 28.
    Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Voigt, H.-M., Ebeling, W., Rechenberg, I., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996). doi: 10.1007/3-540-61723-X_982 CrossRefGoogle Scholar
  29. 29.
    Rodríguez, N.: Feature relevance estimation by evolving probabilistic dependency networks and weighted kernel machine. A thesis submitted to the District University Francisco José de Caldas in fulfilment of the requirements for the degree of Master of Science in Information and Communications, Colombia, pp. 3–4 (2013)Google Scholar
  30. 30.
    Bengoetxea, E., Larrañaga, P., Bloch, I., Perchant, A.: Estimation of distribution algorithms: a new evolutionary computation approach for graph matching problems. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds.) EMMCVPR 2001. LNCS, vol. 2134, pp. 454–469. Springer, Heidelberg (2001). doi: 10.1007/3-540-44745-8_30 CrossRefGoogle Scholar
  31. 31.
    Pelikan, M., Sastry, K., Cantú-Paz, E. (eds.): Scalable Optimization via Probabilistic Modeling: From Algorithms to Application. SCI, vol. 33. Springer, Heidelberg (2006). doi: 10.1007/978-3-540-34954-9 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.District University “Francisco José de Caldas”BogotáColombia

Personalised recommendations