Abstract
The basic k-nearest-neighbor classification algorithm works well in many domains but has several shortcomings. This paper proposes a tolerant instance-based learning algorithm TIBL and it’s combining method by simple voting of TIBL, which is an integration of genetic algorithm, tolerant rough sets and k-nearest neighbor classification algorithm. The proposed algorithms seek to reduce storage requirement and increase generalization accuracy when compared to the basic k-nearest neighbor algorithm and other learning models. Experiments have been conducted on some benchmark datasets from the UCI Machine Learning Repository. The results show that TIBL algorithm and it’s combining method, improve the performance of the k-nearest neighbor classification, and also achieves higher generalization accuracy than other popular machine learning algorithms.
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
Pawlak, Z.: ROUGH SETS: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht (1991)
Cover, T.M., Hart, P.E.: Nearest Neighbor Pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Databases. University of California Irvine, Department of Information and Computer Science, Irvine, CA (1998), Internet: http://www.ics.uci.edu/mlearn/MLRepository.html
Guvenir, H.A., Akkus, A.: Weighted K-Nearest Neighbor Classification on Feature Projections. In: Proc. 12th Intel. Symposium on Computer and Information Sciences (ISCIS 1997), Antalya, Turkey, pp. 44–51 (1997)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Machine Learning 6, 37–66 (1991)
Aha, D.W.: Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithm. International Journal of Man-Machine Studies 36(1), 267–287 (1992)
Rachlin, J., Kasif, S., Salzberg, S., Aha, D.W.: Towards a Better Understanding of Memory-Based and Bayesian Classifiers. In: Proc. Eleventh International Machine Learning Conference, pp. 242–250. Morgan Kaufmann, New Brunswick (1994)
Wilson, D.R., Martinez, T.R.: An Integrated Instance-Based Learning Algorithm. Computational Intelligence 16(1), 1–28 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bao, Y., Tsuchiya, E., Ishii, N., Du, X. (2005). Classification by Instance-Based Learning Algorithm. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_18
Download citation
DOI: https://doi.org/10.1007/11508069_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
eBook Packages: Computer ScienceComputer Science (R0)