Abstract
The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification tasks. Based on the neighborhood concept, several classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification rules derived from them are used in several real data classification tasks. Also, some voting ensembles of classifiers based on these new rules have been tested and compared.
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References
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley, Chichester (2001)
Bohm, C., Krebs, F.: High performance data mining using the nearest neighbor join. In: Second IEEE International Conference on Data Mining, pp. 43–50 (2002)
Dasarathy, B.V.: Data mining tasks and methods: Classification: nearest-neighbor approaches. In: Handbook of data mining and knowledge discovery, pp. 288–298. Oxford University Press, Oxford (2002)
Katayama, N., Satoh, S.: Distinctiveness-Sensitive Nearest-Neighbor search for efficient similarity retrieval of multimedia information. In: Proceedings of the 17th International Conference on Data Engineering, pp. 493–502 (2001)
Sanchez, J.S., Pla, F., Ferri, F.J.: On the use of neighbourhood-based non-parametric classifiers. Pattern Recognition Letters 18, 1179–1186 (1997)
Moreno-Seco, F., Micó, L., Oncina, J.: Extending fast nearest neighbour search algorithms for approximate k-NN classification. In: Perales, F.J., Campilho, A.C., Pérez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 589–597. Springer, Heidelberg (2003)
Jamonczyk, J.W., Toussaint, G.T.: Relative neighbourhood graphs and their relatives. Proceedings IEEE 80, 1502–1517 (1992)
Wagner, R.A., Fischer, M.J.: The String-to-String Correction Problem. Journal of the Association for Computing Machinery 21(1), 168–173 (1974)
Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley Interscience, Hoboken (2004)
Selfridge, O.: Pandemonium: a paradigm for learning in mechanisation of thought processes. In: Proceedings of a Symposium Held at the National Physical Laboratory, pp. 513–526 (1958)
Van Erp, M., Vuurpijl, L.G., Schomaker, L.R.B.: An overview and comparison of voting methods for pattern recognition. In: Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition, pp. 195–200 (2002)
de Borda, J.-C.: Memoire sur les Elections au Scrutin. Histoire de l’Academie Royale des Sciences, Paris (1781)
Duin, R.: A theoretical study of six classifier fusion strategies. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 281–286 (2002)
Lundsteen, C., Phillip, J., Granum, E.: Quantitative analysis of 6985 digitized trypsin G-banded human metaphase chromosomes. Clinical Genetics 18, 355–370 (1980)
Granum, E., Thomason, M.G., Gregor, J.: On the use of automatically inferred Markov networks for chromosome analysis. In: Lundsteen, C., Piper, J. (eds.) Automation of Cytogenetics, pp. 233–251. Springer, Heidelberg (1989)
Granum, E., Thomason, M.G.: Automatically inferred Markov network models for classification of chromosomal band pattern structures. Cytometry 11, 26–39 (1990)
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Mazón, JN., Micó, L., Moreno-Seco, F. (2007). New Neighborhood Based Classification Rules for Metric Spaces and Their Use in Ensemble Classification. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_46
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DOI: https://doi.org/10.1007/978-3-540-72847-4_46
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