Abstract
This paper proposes a new framework for incremental learning based on rule layers constrained by inequalities of accuracy and coverage. Since the addition of an example is classified into one of four possibili- ties, four patterns of an update of accuracy and coverage are observed, which give two important inequalities of accuracy and coverage for induction of probabilistic rules. By using these two 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. If a new example contributes to an increase in the accuracy and coverage of a formula in the subrule layer, the formula is moved into the rule layer. If this contributes to a decrease of a formula in the rule layer, the formula is moved into the subrule layer. The proposed method was evaluated on datasets regarding headaches and meningitis, and the 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)
Skowron, A., Grzymala-Busse, J.: From rough set theory to evidence theory. In: Yager, R., Fedrizzi, M., Kacprzyk, J. (eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 193–236. John Wiley & Sons, New York (1994)
Pawlak, Z.: Rough Sets. Kluwer Academic Publishers, Dordrecht (1991)
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: [16], pp. 70–79
Tsumoto, S., Takabayashi, K.: Data mining in meningoencephalitis: The starting point of discovery challenge. In: [16], 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)
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. (2012). Incremental Rules Induction Method Based on Three Rule Layers. In: Chen, L., Felfernig, A., Liu, J., RaÅ›, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2012. Lecture Notes in Computer Science(), vol 7661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34624-8_8
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DOI: https://doi.org/10.1007/978-3-642-34624-8_8
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