Abstract
Several algorithms for induction of decision trees have been developed to solve problems with large datasets, however some of them have spatial and/or runtime problems using the whole training sample for building the tree and others do not take into account the whole training set. In this paper, we introduce a new algorithm for inducing decision trees for large numerical datasets, called IIMDT, which builds the tree in an incremental way and therefore it is not necesary to keep in main memory the whole training set. A comparison between IIMDT and ICE, an algorithm for inducing decision trees for large datasets, is shown.
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Franco-Arcega, A., Carrasco-Ochoa, J.A., Sánchez-Díaz, G., Martínez-Trinidad, J.F. (2008). A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_36
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DOI: https://doi.org/10.1007/978-3-540-88906-9_36
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