Abstract
Classification is an important research topic in knowledge discovery and data mining. Many different classifiers have been motivated and developed of late years. In this paper, we propose an effective scheme for learning multicategory classifiers based on genetic programming. For a k-class classification problem, a training strategy called adaptive incremental learning strategy and a new fitness function are used to generate k discriminant functions. We urge the discriminant functions to map the domains of training data into a specified interval, and thus data will be assigned into one of the classes by the values of functions. Furthermore, a Z-value measure is developed for resolving the conflicts. The experimental results show that the proposed GP-based classification learning approach is effective and performs a high accuracy of classification.
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
Bojarczuk, C. C, Lopes, H. S., Freitas, A. A.: Discovering comprehensible classification rules using genetic programming: a case study in a medical domain. Proc. Genetic and Evolutionary Computation Conf. (GECCO-99). Orlando, FL, USA (1999) 953–958
Chen, K. H. et al.: A multiclass neural network classifier with fuzzy teaching inputs. Fuzzy Sets System, Vol. 91, no. 1 (1997) 15–35
Duda, R. O., Hart, P. E.: Pattern classification and scene analysis, John Wiley & Sons (1973)
Fisher, R. A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics, pt. II, Vol. 7(1936)179–188
Freitas, A. A.: A genetic programming framework for two data mining tasks: classification and generalized rule induction. Proc. 2nd Annual Conf. Morgan Kaufmann (1997) 96–101
Han, E. H., Karypis, G., Kumar, V: Text categorization using weight adjusted k-nearest neighbor classification. PhD thesis, University of Minnesota (1999)
Heckerman, D., Wellman, M. P.: Bayesian networks. Communications of the ACM, Vol. 38, No. 3 (1995)
Hunt, E. B., Marin, J., Stone, P. J.: Experiments in induction. Academic Press (1966)
Kishore, J. K., Patnaik, L. M., Mani, V., Agrawal, V. K.: Application of genetic programming for multicategory pattern classification. IEEE Trans, on Evolutionary Computation, Vol. 4, No. 3 (2000) 242–258
Kotani, M., Ozawa, S., Nakai, M., Akazawa, K.: Emergence of feature extraction function using genetic programming. Proc. Third Int. Conf. on Knowledge-Based Intelligent Information Engineering System (1999) 149–152
Koza, J. R.: Genetic Programming: On the programming of computers by means of Natural Selection. MIT Press (1992)
Koza, J. R.: Introductory genetic programming tutorial. Genetic Programming 1996 Conf. Stanford University (1996)
Kulkarni, S. R., Lugosi, G., Venkatesh, S. S.: Learning pattern classification-a survey. IEEE Trans. on Information Theory, Vol. 44, No. 6 (1998) 2178–2206
Lee, H. M.: A neural network classifier with disjunctive fuzzy information. Neural Networks, Vol. 11, No. 6 (1998) 1113–1125
Lee, H. M., Chen, C. M., Chen, J. M., Jou, Y. L.: An efficient fuzzy classifier with feature selection based on fuzzy entropy. IEEE Trans, on Systems, Man, and Cybernetics-part b: Cybernetics, Vol. 31, No. 3 (2001) 426–432
Lovel, B. C, Bradley, A. P.: The multiscale classifier. IEEE Trans, on Pattern Analysis Machine Intelligence, Vol. 18 (1996) 124–137
Mangasarian, O. L., Wolberg, W. H.: Cancer diagnosis via linear programming. SIAM News, Vol. 23, No. 5 (1990) 1–18
Nauck, D., Kruse, R.: A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets System, Vol. 89, No. 3 (1997) 277–288
Quinlan, J. R.: Induction of decision trees. Machine Learning, 1: (1986) 81–106
Ross, K. A., Wright, C. R. B.: Discrete Mathematics. Prentice-Hall, Inc. (1992)
Setiono, R. et al., “Neural-network feature selector”, IEEE Trans, on Neural Networks, Vol. 8 (1997) 654–662
Sherrah, J., Bogner, R. E., Bouzerdoum, A.: Automatic selection of features for classification using genetic programming. in Proc. Australian New Zealand Conf. On Intelligent Information Systems (1996) 284–287
Simpson, P. K.: Fuzzy Min-Max neural networks—Part 1: Classification. IEEE Trans. on Neural Networks, Vol. 3 (1992) 776–786
Singleton, A.: Genetic programming with C++. Byte, Feb (1994) 171–176
Wang, C. H., Liu, J. E, Hong, T. P., Tseng, S. S.: A fuzzy inductive learning strategy for modular rules. Fuzzy Set and Systems (1999) 91–105
Wang, C. H., Hong, T. P., Tseng, S. S.: Integrating fuzzy knowledge by genetic algorithms. IEEE Trans, on Evolutionary Computation, Vol. 2, No. 4 (1998) 138–149
Wang, C. H., Hong, T. P., Tseng, S. S., Liao, C. M.: Automatically integrating multiple rule sets in a distributed-knowledge environment. IEEE Trans. On Systems, Man, And Cybernetics Part C: Applications and Reviews, Vol. 28, No. 3 (1998) 471–476
Yoshida, T., Omatu, S.: Neural network approach to land cover mapping. IEEE Trans. on Geosciences Remote Sensing, Vol. 32, No. 5 (1994) 1103–1108
Zhang, G. P.: Neural networks for classification: a survey. IEEE Trans. on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol. 30, No. 4 (2000) 451–462
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, JY., Chien, BC., Hong, TP. (2002). A Function-Based Classifier Learning Scheme Using Genetic Programming. In: Chen, MS., Yu, P.S., Liu, B. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2002. Lecture Notes in Computer Science(), vol 2336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47887-6_9
Download citation
DOI: https://doi.org/10.1007/3-540-47887-6_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43704-8
Online ISBN: 978-3-540-47887-4
eBook Packages: Springer Book Archive