Abstract
We have already proposed an island model for parallel distributed implementation of fuzzy genetics-based machine learning (GBML) algorithms. As in many other island models, a population of individuals is divided into multiple subpopulations. Each subpopulation is assigned to a different island. The main characteristic feature of our model is that training patterns are also divided into multiple training data subsets. Each subset is assigned to a different island. The assigned subset is used to train the subpopulation in each island. The assignment of the training data subsets is periodically rotated over the islands (e.g., every 100 generations). A migration operation is also periodically used. Our original intention in the use of such an island model was to decrease the computation time of fuzzy GBML algorithms. In this paper, we propose an idea of using our island model for ensemble classifier design. An ensemble classifier is constructed by choosing the best classifier in each island. Since the subpopulation at each island is evolved using a different training data subset, a different classifier may be obtained from each island to construct an ensemble classifier. This suggests a potential ability of our island model as an ensemble classifier design tool. However, the diversity of the obtained classifiers from multiple islands seems to be decreased by frequent training data subset rotation and frequent migration. In this paper, we examine the effects of training data subset rotation and migration on the performance of designed ensemble classifiers through computational experiments.
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
Thrift, P.: Fuzzy Logic Synthesis with Genetic Algorithms. In: Proc. of 4th International Conference on Genetic Algorithms, pp. 509–513 (1991)
Karr, C.L.: Design of an Adaptive Fuzzy Logic Controller using a Genetic Algorithm. In: Proc. of 4th International Conference on Genetic Algorithms, pp. 450–457 (1991)
Karr, C.L., Gentry, E.J.: Fuzzy Control of pH using Genetic Algorithms. IEEE Trans. on Fuzzy Systems 1, 46–53 (1993)
Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Selecting Fuzzy If-Then Rules for Classification Problems using Genetic Algorithms. IEEE Trans. on Fuzzy Systems 3, 260–270 (1995)
Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten Years of Genetic Fuzzy Systems: Current Framework and New Trends. Fuzzy Sets and Systems 141, 5–31 (2004)
Herrera, F.: Genetic Fuzzy Systems: Status, Critical Considerations and Future Directions. International Journal of Computational Intelligence Research 1, 59–67 (2005)
Herrera, F.: Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects. Evolutionary Intelligence 1, 27–46 (2008)
Cordón, O.: A Historical Review of Evolutionary Learning Methods for Mamdani-Type Fuzzy Rule-Based Systems: Designing Interpretable Genetic Fuzzy Systems. International J. of Approximate Reasoning 52, 894–913 (2011)
Ishibuchi, H., Murata, T., Turksen, I.B.: Single-Objective and Two-Objective Genetic Algorithms for Selecting Linguistic Rules for Pattern Classification Problems. Fuzzy Sets and Systems 89, 135–150 (1997)
Ishibuchi, H., Nakashima, T., Murata, T.: Three-Objective Genetics-Based Machine Learning for Linguistic Rule Extraction. Information Sciences 136, 109–133 (2001)
Ishibuchi, H.: Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions. In: Proc. of 2007 IEEE International Conference on Fuzzy Systems, pp. 913–918 (2007)
Fazzolari, M., Alcalá, R., Nojima, Y., Ishibuchi, H., Herrera, F.: A Review of the Application of Multi-Objective Genetic Fuzzy Systems: Current Status and Further Directions. IEEE Trans. on Fuzzy Systems (to appear)
Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer (2002)
Bull, L., Bernado-Mansilla, E., Holmes, J.: Learning Classifier Systems in Data Mining. Springer (2008)
GarcÃa, S., Fernández, A., Luengo, J., Herrera, F.: A Study of Statistical Techniques and Performance Measures for Genetics-Based Machine Learning: Accuracy and Interpretability. Soft Computing 13, 959–977 (2009)
Fernández, A., GarcÃa, S., Luengo, J., Bernadó-Mansilla, E., Herrera, F.: Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study. IEEE Trans. on Evolutionary Computation 14, 913–941 (2010)
Ishibuchi, H., Yamamoto, T., Nakashima, T.: Hybridization of Fuzzy GBML Approaches for Pattern Classification Problems. IEEE Trans. on Systems, Man, and Cybernetics - Part B 35, 359–365 (2005)
Ishibuchi, H., Nojima, Y.: Analysis of Interpretability-Accuracy Tradeoff by Multiobjective Fuzzy Genetics-Based Machine Learning. International J. of Approximate Reasoning 44, 4–31 (2007)
Abadeh, M.S., Habibi, J., Lucas, C.: Intrusion Detection using a Fuzzy Genetics-Based Learning Algorithm. Journal of Network and Computer Applications 30, 414–428 (2007)
Orriols-Puig, A., Casillas, J., Bernadó-Mansilla, E.: Genetic-Based Machine Learning Systems are Competitive for Pattern Recognition. Evolutionary Intelligence 1, 209–232 (2008)
Nojima, Y., Ishibuchi, H., Kuwajima, I.: Parallel Distributed Genetic Fuzzy Rule Selection. Soft Computing 13, 511–519 (2009)
Nojima, Y., Mihara, S., Ishibuchi, H.: Parallel Distributed Implementation of Genetics-Based Machine Learning for Fuzzy Classifier Design. In: Deb, K., Bhattacharya, A., Chakraborti, N., Chakroborty, P., Das, S., Dutta, J., Gupta, S.K., Jain, A., Aggarwal, V., Branke, J., Louis, S.J., Tan, K.C. (eds.) SEAL 2010. LNCS, vol. 6457, pp. 309–318. Springer, Heidelberg (2010)
Ishibuchi, H., Mihara, S., Nojima, Y.: Training Data Subdivision and Periodical Rotation in Hybrid Fuzzy Genetics-Based Machine Learning. In: Proc. of 10th International Conference on Machine Learning and Applications, pp. 229–234 (2011)
Ishibuchi, H., Mihara, S., Nojima, Y.: Parallel Distributed Hybrid Fuzzy GBML Models with Rule Set Migration and Training Data Rotation. IEEE Trans. on Fuzzy Systems (to appear)
Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed Representation of Fuzzy Rules and Its Application to Pattern Classification. Fuzzy Sets and Systems 52, 21–32 (1992)
Cordón, O., del Jesus, M.J., Herrera, F.: A Proposal on Reasoning Methods in Fuzzy Rule-Based Classification Systems. International J. of Approximate Reasoning 20, 21–45 (1999)
Ishibuchi, H., Nakashima, T., Morisawa, T.: Voting in Fuzzy Rule-Based Systems for Pattern Classification Problems. Fuzzy Sets and Systems 103, 223–238 (1999)
Ishibuchi, H., Yamamoto, T.: Rule Weight Specification in Fuzzy Rule-Based Classification Systems. IEEE Trans. on Fuzzy Systems 13, 428–435 (2005)
Alba, E., Tomassini, M.: Parallelism and Evolutionary Algorithms. IEEE Trans. on Evolutionary Computation 6, 443–462 (2002)
Nedjah, N., Alba, E., de Macedo Mourelle, L.: Parallel Evolutionary Computations. Springer, Berlin (2006)
Ruciński, M., Izzo, D., Biscani, F.: On the Impact of the Migration Topology on the Island Model. Parallel Computing 36, 555–571 (2010)
Araujo, L., Merelo, J.: Diversity through Multiculturality: Assessing Migrant Choice Policies in an Island Model. IEEE Trans. on Evolutionary Computation 15, 456–469 (2011)
Luque, G., Alba, E.: Parallel Genetic Algorithms: Theory and Real World Applications. Springer, Berlin (2011)
Candan, C., Goëffon, A., Lardeux, F., Saubion, F.: A Dynamic Island Model for Adaptive Operator Selection. In: Proc. of 2012 Genetic and Evolutionary Computation Conference, Philadelphia, pp. 1253–1260 (2012)
KEEL dataset repository, http://keel.es/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ishibuchi, H., Yamane, M., Nojima, Y. (2012). Ensemble Fuzzy Rule-Based Classifier Design by Parallel Distributed Fuzzy GBML Algorithms. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-34859-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34858-7
Online ISBN: 978-3-642-34859-4
eBook Packages: Computer ScienceComputer Science (R0)