Abstract
The selection and evaluation task of attributes is of great importance for knowledge-based systems. It is also a critical factor affecting systems’ performance. By using the genetic operator as the searching approach and correlation-based heuristic strategy as the evaluating mechanism, this paper presents a GA-CFS method to select the optimal subset of attributes from a given case library. Based on the above, the classification performance is evaluated by employing the combination method of C4.5 algorithm with k-fold cross validation. The comparative experimental results indicate that the proposed method is capable of identifying the most related subset for classification and prediction with reducing the representation space of the attributes dramatically whilst hardly decreasing the classification precision.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yuan, C.A., Tang, C.J., Zuo, J., et al.: Attribute reduction function mining algorithm based on gene expression programming. In: 5th International Conference on Machine Learning and Cybernetics, vol. 1-7, Aug. 13-16, 2006, pp. 1007–1012 (2006)
Hsu, W.H.: Genetic wrappers for feature reduction in decision tree induction and variable ordering in Bayesian network structure learning. Information Sciences 163, 103–122 (2004)
Zhao, Y., Liu, W.Y.: GA-based feature reduction method. Computer engineering and application 15, 52–54 (2004)
Kohavi, R., John, G.H.: Wrappers for feature subset reduction. Artificial Intelligence, 273–324 (1997)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)
Hall, M.A.: Correlation-based feature reduction for discrete and numeric class machine learning. In: Proc. of the 17th International Conference on Machine Learning (2000)
Zhou, M., Sun, S.D.: GA principle and application. National defense industry press, Beijing (1999)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model reduction. In: Wermter, S., Riloff, E., Scheler, G. (eds.) The Fourteenth International Joint Conference on Artificial Intelligence (IJCAI), pp. 1137–1145. Morgan Kaufmann, San Francisco (1995)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Ni, Z., Li, F., Yang, S., Liu, X., Zhang, W., Luo, Q. (2007). Attributes Reduction Based on GA-CFS Method. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_89
Download citation
DOI: https://doi.org/10.1007/978-3-540-72524-4_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72483-4
Online ISBN: 978-3-540-72524-4
eBook Packages: Computer ScienceComputer Science (R0)