Abstract
K nearest neighbor algorithm (k-NN) is an instance-based lazy classifier that does not need to delineate the entire boundaries between classes. Thus some classification tasks that constantly need a training procedure may favor k-NN if high efficiency is needed. However, k-NN is prone to be affected by the underlying data distribution. In this paper, we define a new neighborhood relationship, called passive nearest neighbors, which is deemed to be able to counteract with the variation of data densities. Based on which we develop a new classifier called active and passive nearest neighbor algorithm (APNNA). The classifier is evaluated by 10-fold cross-validation on 10 randomly chosen benchmark datasets. The experimental results show that APNNA performs better than other classifiers on some datasets and worse on some other datasets, indicating that APNNA is a good complement to the current state-of-the-art of classification.
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
Dasarathy, V. (ed.): Nearest Neighbor (NN) Norms: Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1991)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inform. Theory IT-13(1), 21–27 (1967)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth and Brooks/Cole Advanced Books and Software, Monterey, CA (1984)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings to the Thirteenth International Conference, pp. 165–456 (1996)
John, G.C., Leonard, E.T.: K*: An Instance- based Learner Using an Entropic Distance Measure. In: Proceedings of the 12th International Conference on Machine Learning, pp. 108–114 (1995)
Frank, E., Hall, M., Pfahringer, B.: Locally Weighted Naive Bayes. Working Paper 04/03, Department of Computer Science, University of Waikato (2003)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://archive.ics.uci.edu/ml
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Rahman, A.F.R., Alam, H., Fairhurst, M.C.: Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variation, pp. 167–178. Springer, Berlin (2002)
Chen, L., Lu, L., Feng, K., et al.: Multiple classifier integration for the prediction of protein structural classes. Journal of Computational Chemistry 30, 2248–2254 (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
Feng, K., Gao, J., Feng, K., Liu, L., Li, Y. (2012). Active and Passive Nearest Neighbor Algorithm: A Newly-Developed Supervised Classifier. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-25944-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25943-2
Online ISBN: 978-3-642-25944-9
eBook Packages: Computer ScienceComputer Science (R0)