Abstract
Memory base learning is one of main fields in the area of machine learning. We propose a new methodology for addressing the classification task that relies on the main idea of the k – nearest neighbors algorithm, which is the most important representative of this field. In the proposed approach, given an unclassified pattern, a set of neighboring patterns is found, but not necessarily using all input feature dimensions. Also, following the concept of the naïve Bayesian classifier, we adopt the hypothesis of the independence of input features in the outcome of the classification task. The two concepts are merged in an attempt to take advantage of their good performance features. In order to further improve the performance of our approach, we propose a novel weighting scheme of the memory base. Using the self-organizing maps model during the execution of the algorithm, dynamic weights of the memory base patterns are produced. Experimental results have shown superior performance of the proposed method in comparison with the aforementioned algorithms and their variations.
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References
Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Machine Learning 6, 37–66 (1991)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California, Irvine (1998), http://www.ics.uci.edu/_mlearn/MLRepository.html
Cost, S., Salzberg, S.: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning 10, 57–78 (1993)
Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1991)
Demiroz, G., Guvenir, H.A.: Classification by Voting Feature Intervals. In: Proceedings of the 9th European Conference on Machine Learning, Prague (1997)
Fan, H., Ramamohanarao, K.: A Bayesian Approach to Use Emerging Patterns for Classification. In: Proceedings of the 14th Australasian Database Conference, Adelaide (2003)
Guvenir, H.A., Akkus¸, A.: Weighted K Nearest Neighbor Classification on Feature Projections. In: Proceedings of the 12-th International Symposium on Computer and Information Sciences, Antalya, Turkey (1997)
Hammerton, J., Erik, F.: Combining a self-organising map with memorybased learning. In: Conference on Computational Natural Language Learning (CoNLL), Toulouse, France, July 6-7, pp. 9–14 (2001)
Kohonen, T.: Self-Organizing Maps. In: Information Sciences, 2nd edn. Springer, Heidelberg (1997)
Kononenko, I.: Naive Bayesian classifier and continuous attributes. Informatica 16(1), 1–8 (1992)
Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Statistics 33, 1065–1076 (1962)
Pateritsas, C., Pertselakis, M., Stafylopatis, A.: A SOM-based classifier with enhanced structure learning. In: Proceedings of the IEEE International Conference on Systems, Man & Cybernetics, The Hague, Netherlands, October 10-13, pp. 4832–4837 (2004)
Pateritsas, C., Stafylopatis, A.: Independent Nearest Features Memory-Based Classifier. In: International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), Vienna, Austria, November 28-30, vol. 2, pp. 781–786 (2005)
Rauber, A.: LabelSOM: On the labeling of self-organizing maps. In: Proceedings of International Joint Conference on Neural Networks, Washington, DC (1999)
Stanfill, C., Waltz, D.: Toward memory-based reasoning. Communications of the ACM 29(12), 1213–1228 (1986)
Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics 6(6), 448–452 (1976)
Xin, T., Ozturk, P., Gu, M.: Dynamic feature weighting in nearest neighbor classifiers. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, August 26-29, vol. 4, pp. 2406–2411 (2004)
Vesanto, J.: Using SOM in Data Mining. Licentiate’s thesis in the Helsinki University of Technology (2000)
Wettschereck, D., Aha, W.D.: Weighting Features. In: First International Conference on Case-Based Reasoning, Lisbon, Portugal, pp. 347–358. Springer, Heidelberg (1995)
Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)
Wilson, D.R., Martinez, T.R.: Reduction Techniques for Instance-Based Learning Algorithm. In: Machine Learning, vol. 38, pp. 257–286. Kluwer Academic Publishers, Dordrecht (2000)
Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics 2(3), 408–421 (1972)
Yang, Y., Webb, G.I.: Proportional k-interval discretization for naive-Bayes classifiers. In: Proceedings of the 12th European Conference on Machine Learning, pp. 564–575 (2001)
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Pateritsas, C., Stafylopatis, A. (2006). A Nearest Features Classifier Using a Self-organizing Map for Memory Base Evaluation. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_40
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DOI: https://doi.org/10.1007/11840930_40
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