Abstract
This paper proposes a new framework for incremental learning based on incremental sampling scheme. Since the addition of an example is classified into one of four possibilities, four patterns of an update of accuracy and coverage are observed, which give four important inequalities of accuracy and coverage. By using these inequalities, the proposed method classifies a set of formulae into three layers: the rule layer, subrule layer and the non-rule layer. Then, the obtained rule and subrule layers play a central role in updating rules. The proposed method was evaluated on datasets regarding headaches, whose results show that the proposed method outperforms the conventional methods.
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References
Breiman, L., Freidman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)
Cestnik, B., Kononenko, I., Bratko, I.: Assistant 86: A knowledge-elicitation tool for sophisticated users. In: EWSL, pp. 31–45 (1987)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
Clark, P., Niblett, T.: The cn2 induction algorithm. Machine Learning 3 (1989)
Michalski, R.S.: A theory and methodology of inductive learning. Artif. Intell. 20(2), 111–161 (1983)
Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning system aq15 and its testing application to three medical domains. In: AAAI, pp. 1041–1047 (1986)
Shan, N., Ziarko, W.: Data-based acqusition and incremental modification of classification rules. Computational Intelligence 11, 357–370 (1995)
Utgoff, P.E.: Incremental induction of decision trees. Machine Learning 4, 161–186 (1989)
Quinlan, J.: C4.5 - Programs for Machine Learning. Morgan Kaufmann, Palo Alto (1993)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)
Tsumoto, S.: Incremental rule induction based on rough set theory. In: [15], pp. 70–79
Tsumoto, S., Takabayashi, K.: Data mining in meningoencephalitis: The starting point of discovery challenge. In: [15], pp. 133–139
Tsumoto, S., Tanaka, H.: Primerose: Probabilistic rule induction method based on rough sets and resampling methods. Computational Intelligence 11, 389–405 (1995)
Skowron, A., Rauser, C.: The discerniblity matrix and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Application and Advances of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.): ISMIS 2011. LNCS, vol. 6804. Springer, Heidelberg (2011)
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Tsumoto, S., Hirano, S. (2013). Incremental Induction of Medical Diagnostic Rules. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds) Brain and Health Informatics. BHI 2013. Lecture Notes in Computer Science(), vol 8211. Springer, Cham. https://doi.org/10.1007/978-3-319-02753-1_41
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DOI: https://doi.org/10.1007/978-3-319-02753-1_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02752-4
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