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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Tsumoto, S., Hirano, S. (2012). Incremental Rules Induction Method Based on Three Rule Layers. In: Chen, L., Felfernig, A., Liu, J., Raś, 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
Publisher Name: Springer, Berlin, Heidelberg
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