Abstract
Nearest neighbor is one of the most used techniques for performing classification tasks. However, its simplest version has several drawbacks, such as low efficiency, storage requirements and sensitivity to noise. Prototype generation is an appropriate process to alleviate these drawbacks that allows the fitting of a data set for nearest neighbor classification. In this work, we present an extension of our previous proposal called IPADE, a methodology to learn iteratively the positioning of prototypes using a differential evolution algorithm. In this extension, which we have called IPADECS, a complete solution is codified in each individual. The results are contrasted with non-parametrical statistical tests and show that our proposal outperforms previously proposed methods.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)
Garcia, E.K., Feldman, S., Gupta, M.R., Srivastava, S.: Completely Lazy Learning. IEEE Transactions on Knowledge and Data Engineering 22(9), 1274–1285 (2010)
Liu, H., Motoda, H. (eds.): Instance Selection and Construction for Data Mining. The International Series in Engineering and Computer Science. Springer, Heidelberg (2001)
García, S., Cano, J.R., Herrera, F.: A memetic algorithm for evolutionary prototype selection: A scaling up approach. Pattern Recognition 41(8), 2693–2709 (2008)
Lam, W., Keung, C., Liu, D.: Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1075–1090 (2002)
Chang, C.: Finding Prototypes For Nearest Neighbor Classifiers. IEEE Transactions on Computers 23(11), 1179–1184 (1974)
Chen, C.H., Jóźwik, A.: A sample set condensation algorithm for the class sensitive artificial neural network. Pattern Recognition Letters 17(8), 819–823 (1996)
Sánchez, J.S.: High training set size reduction by space partitioning and prototype abstraction. Pattern Recognition 37(7), 1561–1564 (2004)
Kohonen, T.: The self organizing map. Proceedings of the IEEE 78(9), 1464–1480 (1990)
Cervantes, A., Galván, G., Isasi, I.M.: AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 39(5), 1082–1091 (2009)
Triguero, I., García, S., Herrera, F.: IPADE: Iterative Prototype Adjustment for Nearest Neighbor Classification. IEEE Transactions on Neural Networks 21(12), 1984–1990 (2010)
Ferrante, N., Tirronen, V.: Scale factor local search in differential evolution. Memetic Computing 1(2), 153–171 (2009)
Alcalá-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. Journal of Multiple-Valued Logic and Soft Computing (2010) ( in press)
Alpaydin, E.: Introduction to Machine Learning, 2nd edn. The MIT Press, Cambridge (2010)
García, S., Herrera, F.: An Extension on ”Statistical Comparisons of Classifiers over Multiple Data Sets” for all Pairwise Comparisons. Journal of Machine Learning Research 9, 2677–2694 (2008)
García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental Analysis of Power. Information Sciences 180, 2044–2064 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Triguero, I., García, S., Herrera, F. (2011). Enhancing IPADE Algorithm with a Different Individual Codification. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-21222-2_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21221-5
Online ISBN: 978-3-642-21222-2
eBook Packages: Computer ScienceComputer Science (R0)