A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets

Authors:
Anilu Franco-Arcega;J. Ariel Carrasco-Ochoa;Guillermo Sánchez-Díaz;J. Fco Martínez-Trinidad
Affiliations:
Computer Science Department National Institute of Astrophysics, Optics and Electronics, Santa Maria Tonantzintla, Mexico C.P.72840;Computer Science Department National Institute of Astrophysics, Optics and Electronics, Santa Maria Tonantzintla, Mexico C.P.72840;Centro Universitario de los Valles, Universidad de Guadalajara, Ameca, Mexico C.P. 46600;Computer Science Department National Institute of Astrophysics, Optics and Electronics, Santa Maria Tonantzintla, Mexico C.P.72840
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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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.