Abstract
The nearest neighbor rule is one of the most representative methods in data mining. In recent years, a great amount of proposals have arisen for improving its performance. Among them, instance selection is highlighted due to its capabilities for improving the accuracy of the classifier and its efficiency simultaneously, by editing noise and reducing considerably the size of the training set. It is also possible to remark the role of feature and instance weighting as outstanding methodologies for improving further the performance of the nearest neighbor rule.
In this work we present a new co-evolutionary algorithm for combining the former techniques. Its performance is compared with evolutionary approaches performing instance selection, instance weighting and feature weighting in isolation, as well as with the nearest neighbor classifier. The results obtained, contrasted through nonparametric statistical tests, supports the capabilities of co-evolution as a outstanding strategy for joining several proposals for enhancing the nearest neighbor rule.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J., Herrera, F.: KEEL: A software tool to assess evolutionary algorithms to data mining problems. Soft Computing 13, 307–318 (2009)
Alcalá-Fdez, J., Fernández, 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 17(2-3), 255–287 (2011)
Atkeson, C.G., Moore, A.W., Schaal, S.: Locally weighted learning. Artificial Intelligence Review 11, 11–73 (1997)
Cano, J.R., Herrera, F., Lozano, M.: Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study. IEEE Transactions on Evolutionary Computation 7(6), 561–575 (2003)
Derrac, J., García, S., Herrera, F.: IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule. Pattern Recognition 43(6), 2082–2105 (2010)
Eshelman, L.J.: The CHC adaptative search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms, pp. 265–283. Morgan Kaufmann, San Mateo (1991)
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)
García, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 417–435 (2012)
Ghosh, A., Jain, L.C. (eds.): Evolutionary Computation in Data Mining. Springer, Heidelberg (2005)
Liu, H., Motoda, H. (eds.): Instance Selection and Construction for Data Mining, ser. The Springer International Series in Engineering and Computer Science. Springer, Heidelberg (2001)
Potter, M.A., Jong, K.A.D.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Sánchez, A.M., Lozano, M., Villar, P., Herrera, F.: Hybrid crossover operators with multiple descendents for real-coded genetic algorithms: Combining neighborhood-based crossover operators. International Journal on Intelligent Systems 24(5), 540–567 (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)
Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weigthing methods for a class of lazy learning algorithms. Artificial Intelligence Review 11, 273–314 (1997)
Wolpert, D.H., Macready, W.G.: Coevolutionary free lunches. IEEE Transactions on Evolutionary Computation 9(6), 721–735 (2005)
Wu, X., Kumar, V. (eds.): The Top Ten Algorithms in Data Mining. Data Mining and Knowledge Discovery. Chapman & Hall,CRC (2009)
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
Derrac, J., Triguero, I., García, S., Herrera, F. (2012). A Co-evolutionary Framework for Nearest Neighbor Enhancement: Combining Instance and Feature Weighting with Instance Selection. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28931-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-28931-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28930-9
Online ISBN: 978-3-642-28931-6
eBook Packages: Computer ScienceComputer Science (R0)