Abstract
In the paper, an evolutionary algorithm for global induction of decision trees is presented. In contrast to greedy, top-down approaches it searches for the whole tree at the moment. Specialised genetic operators are proposed which allow modifying both tests used in the non-terminal nodes and structure of the tree. The proposed approach was validated on both artificial and real-life datasets. Experimental results show that the proposed algorithm is able to find competitive classifiers in terms of accuracy and especially complexity.
This work was supported by the grant W/WI/1/02 from Białystok Technical University
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Krętowski, M., Grześ, M. (2005). Global learning of decision trees by an evolutionary algorithm. In: Saeed, K., Pejaś, J. (eds) Information Processing and Security Systems. Springer, Boston, MA. https://doi.org/10.1007/0-387-26325-X_36
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DOI: https://doi.org/10.1007/0-387-26325-X_36
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