Abstract
This article examines the methods how to avoid an overfitting-effect within GeLog-systems. This effect can be observed in nearly all systems of inductive concept learning, if due to false classification of examples false, especially too specific theories, are learned. There are a number or procedures, how to counter the effects of the overfitting-effect or to avoid it. This article develops criteria for the selection of those procedures. In this context, the integrability into the GeLog-system, a system of genetic inductive logic programming, is of great importance. Finally, a filter procedure, based on the correlation heuristic, which is also used for top-down-pruning, is selected, as it promised the possible application to a relatively huge amount of problems. After that, the efficiency of the methods will be proven with the help of systematic experiments.
This scientific work was supported Habilitation Fellowship of the Bavarian Government 1999 and the German Academic Exchange Service (DAAD).
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Kókai, G. (2003). Development of Methods How to Avoid the Overfitting-Effect within the GeLog-System. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_131
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DOI: https://doi.org/10.1007/978-3-540-45226-3_131
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