Abstract
Relational instance-based learning (RIBL) algorithms offer high prediction capabilities. However, they do not scale up well, specially in domains where there is a time bound for classification. Nearest prototype approaches can alleviate this problem, by summarizing the data set in a reduced set of prototypes. In this paper we present an algorithm to build Relational Nearest Prototype Classifiers (rnpc). When compared with RIBL approaches, the algorithm is able to dramatically reduce the number of instances by selecting the most relevant prototypes, maintaining similar accuracy. The number of prototypes is obtained automatically by the algorithm, although it can be also bounded by the user. Empirical results on benchmark data sets demonstrate the utility of this approach compared to other instance based approaches.
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Emde, W., Wettschereck, D.: Relational instance-based learning. In: Proceedings of the Thirteen International Conference on Machine Learning, pp. 122–130. Morgan Kaufmann, San Francisco (1996)
Dzeroski, S., Lavrac, N.: Relational Data Mining. Springer, Heidelberg (2001)
Fernández, F., Isasi, P.: Evolutionary design of nearest prototype classifiers. Journal of Heuristics 10(4), 431–454 (2004)
Kuncheva, L., Bezdek, J.: Nearest prototype classfication: Clustering, genetic algorithms, or random search? IEEE Transactions on Systems, Man, and Cybernetics (1998)
Witten, I., Frank, E.: Data Mining. Practical Machine Learning Tools and Techniques. Elsevier and Morgan Kaufmann (2005)
Kirsten, M., Wrobel, S., Horváth, T.: Distance Based Approaches to Relational Learning and Clustering. In: Relational Data Mining, pp. 213–232. Springer, Heidelberg (2001)
Woznica, A., Kalousis, A., Hilario, M.: Distance-based learning over extended relational algebra structures. In: Proceedings of the 15th International Conference of Inductive Logic Programming (2005)
Hilario, M., Kim, J., Bradley, P., Attwood, T.: Classifying protein fingerprints. In: Proceedings of the 8th Conference on Principles and Practice of Knowledge Discovery in Databases (2004)
Dzeroski, S., Schulze-Kreme, S., Heidtke, K., Siems, K., Wettschereck, D.: Intelligent Data Analysis in Medicine and Pharmacology. In: Diterpenes structure elucidation from 13C NMR spectra with machine learning, pp. 207–225 (1997)
Kalousis, A., Hilario, M.: Representational issues in meta-learning. In: Proceedings of the 20th International Conference on Machine Learning (2003)
Ramón, J., Bruynooghe, M.: A polinomial time computable metric between point sets. In: Acta Informática (2001)
Duda, P.H., Stork, D.: Nonparametric Techniques. In: Pattern Classification, 2nd edn. John Wiley & Sons, Chichester (2001)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Srinivasan, A., King, R., Muggleton, S.: The role of background knowledge: using a problem from chemistry to examine the performance of an ILP program, Under review for Intelligent Data Analysis in Medicine and Pharmacology (1996)
Bolckeel, H.: Top-down induction of first order logical decision trees. PhD thesis, Departament of Computer Science, Katholieke Universiteit Leuven (1998)
Leiva, H., Atramentov, A., Honavar, V.: Experiments with MRDTL – a multirelational decision tree learning algorithm. In: Proceedings of the Workshop on Multi-Relational Decision Tree Learning (2002)
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García-Durán, R., Fernández, F., Borrajo, D. (2008). Prototypes Based Relational Learning. In: Dochev, D., Pistore, M., Traverso, P. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2008. Lecture Notes in Computer Science(), vol 5253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85776-1_12
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DOI: https://doi.org/10.1007/978-3-540-85776-1_12
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