Abstract
Constructive Induction is a preprocessing step applied to representation space prior to machine learning algorithms and transforms the original representation with complex interaction into a representation that highlights regularities and is easy to be learned. In this paper a Fuzzy-GA wrapper-based constructive induction system is represented. In this model an understandable real-coded GA is employed to construct new features and a fuzzy system is designed to evaluate new constructed features and select more relevant features. This model is applied on a PNN classifier as a learning algorithm and results show that integrating PNN classifier with Fuzzy-GA wrapper-based constructive induction module will improve the effectiveness of the classifier.
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.
References
Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons, Chichester (2003)
Zhou, E., Khotanzad, A.: Fuzzy Classifier Design Using Genetic Algorithms. Elsevier, Pattern Recognition 40 (2007)
Bloedorn, E.: Multistrategy Constructive Induction. George Mason University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (1996)
Shila, S.L., Pérez, E.: Constructive Induction and Genetic Algorithms for Learning Concepts with Complex Interaction. ACM, New York (2005)
Matheus, C., Rendell, L.: Constructive Induction On Decision Tree. University of Illinois at Urbana-Champaign (1989)
Hu, Y., Kibler, D.: A Wrapper Approach For Constructive Induction. University of California (1993)
Callan, J.: Knowledge-Based Feature Generation for Inductive Learning. University of Massachusetts (1993)
Fawcett, T.: Feature Discovery for Inductive Concept Learning. University of Massachussets (1993)
Yang, S.: A Scheme for Feature Construction and a Comparison of Empirical Methods. University of Illinois (1991)
Rendell, L., Ragavan, H.: Improving the Design of Induction Methods by Analyzing Algorithm Functionality and Data-Based Concept Complexity. University of Illinois (1993)
Bloedorn, E., Michalski, R.: The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economic. In: Ninth International Symposium on Methodologies for Intelligent Systems (ISMIS-1996), Zakopane, Poland, June 10–13 (1996)
Gang, Q., Garrison, W., Greenwood, D., Liu, C., Sharon, H.: Searching for Multiobjective Preventive MaintenanceSchedules: Combining Preferences with Evolutionary Algorithms. Elsevier, European Journal of Operational Research (2007)
Ghosh, A., Jain, L.C.: Evolutionary Computation in Data Mining. Springer, Heidelberg (2005)
Thomas, B.B.: Evolutionary Computation in Data Mining. Springer, Heidelberg (2006)
Theodoridis, S.: Pattern Recognition, 2nd edn. Elsevier, Amsterdam
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Purdue University (2003)
Wesley, C., Tsau, Y.L.: Foundations and Advances in Data Mining. Springer, Heidelberg (2005)
Yang, Y., Webb, G.I.: A Comparative Study of Discretization Methods for Naive-Bayes Classifiers. In: The 2002 Pacific Rim Knowledge Acquisition Work-shop, Tokyo, Japan (2002)
Watanabe, L., Rendell, L.: Learning Structural Decision Trees From Examples. University of Illinois (1991)
Cho, H.J., Wang, B.H.: Automatic Rule Generation for Fuzzy Controllers Using Genetic Algorithms: a Study on Representation Scheme and Mutation rate. In: IEEE World Congress on Computational Intelligence Fuzzy Systems (1998)
Holland, J.: Adaption in Natural and Artificial. SystemsUniversity of Michigan Press (1975)
Whitley, D.: A genetic algorithm tutorial (1994)
Guo, Z.X., Wong, W.K., Leung, S.Y.S., Fan, J.T., Chan, S.F.: Genetic Optimization of Order Scheduling with Multiple Uncertainties. Elsevier, Expert Systems with Applications 35 (2008)
Chen, S.M., Huang, C.M.: A New Approach to Generate Weighted Fuzzy Rules Using Genetic Algorithms for Estimating Null Values. Elsevier, Expert Systems with Applications 35 (2008)
Chen, L.H., Hsiao, H.D.: Feature Selection to Diagnose a Business Crisis by Using a Real GA-based Support Vector Machine: An Eempirical Study. Elsevier, Expert Systems with Applications 35 (2008)
Hu, Y., Kibler, D.F.: Generation of Attributes for Learning Algorithms. In: Proc. of the Thirteenth National Conference on Artificial Intelligence, pp. 806–811. AAAI, The MIT Press (1996)
Ragavan, H., Rendell, L.A.: Lookahead Feature Construction for Learning Hard Concepts. In: Proc. Of the Tenth International Conference on Machine Learning, June 1993, pp. 252–259. University of Massachusetts, Amherst, MA, USA (1993)
Perez, E.: Learning Despite Complex Interaction: An Approach Based on Relational Operators. PhD thesis, university of Illinois, Urbana-Champaign (1997)
Specht, D.F.: Probabilistic Neural Networks and the Polynomial Adaline as Complementary Techniques for Classification. IEEE Transactions on Neural Networks 1, 111–121 (1990)
Pagallo, G.: Adaptive Decision Tree Algorithms for Learning from Examples. PhD thesis, University of California at Santa Cruz (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
HajAbedi, Z., Kangavari, M.R. (2009). A Fuzzy-GA Wrapper-Based Constructive Induction Model. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-04020-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04019-1
Online ISBN: 978-3-642-04020-7
eBook Packages: Computer ScienceComputer Science (R0)