Abstract
A standard method for determining decision trees is to learn them from examples. A disadvantage of this approach is that once a decision tree is learned, it is difficult to modify it to suit different decision making situations. An attractive approach that avoids this problem is to learn and store knowledge in a declarative form, e.g., as decision rules, and then, whenever needed, generate from it a decision free that is most suitable in any given situation. This paper describes an efficient method for this purpose, called AQDT-1, which takes decision rules generated by the learning system AQ15 and builds from them a decision tree optimized according to a given quality criterion. The method is able to build conventional decision trees, as well as the so-called “skip noder” trees, in which measuring attributes assigned to some nodes may be avoided. It is shown that “skip-node” trees can be significantly simpler than conventional ones. In the experiments comparing AQDT-1 with C4.5, the former outperformed the latter both in terms of the predictive accuracy as well as the simplicity of the generated decision trees.
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© 1993 Springer-Verlag Berlin Heidelberg
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Imam, I.F., Michalski, R.S. (1993). Should decision trees be learned from examples or from decision rules?. In: Komorowski, J., Raś, Z.W. (eds) Methodologies for Intelligent Systems. ISMIS 1993. Lecture Notes in Computer Science, vol 689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56804-2_37
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DOI: https://doi.org/10.1007/3-540-56804-2_37
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