Multivariate decision trees using different splitting attribute subsets for large datasets

Authors:
Anilu Franco-Arcega;José Ariel Carrasco-Ochoa;Guillermo Sánchez-Díaz;José Fco. Martínez-Trinidad
Affiliations:
Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Computational Science and Technology Department, University of Guadalajara, CUValles, Jalisco, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

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Abstract

In this paper, we introduce an incremental induction of multivariate decision tree algorithm, called IIMDTS, which allows choosing a different splitting attribute subset in each internal node of the decision tree and it processes large datasets IIMDTS uses all instances of the training set for building the decision tree without storing the whole training set in memory Experimental results show that our algorithm is faster than three of the most recent algorithms for building decision trees for large datasets.