Creating ensembles of oblique decision trees with...

Details Creating ensembles of oblique decision trees with... Creating ensembles of oblique decision trees with...

2006-06-13

2006-06-13

Corrielus, Jean M. (Department: 2166)

Data processing: database and file management or data structures

Database design

Data structure types

C706S020000, C706S045000

Reexamination Certificate

active

07062504

ABSTRACT:
A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

REFERENCES:
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Cantu-Paz et al. (“Using Evolutionary Algorithms to Induce Oblique Decision Trees,” Lawrence Livermore National Laboratory, accepted for publication in the Genetic and Evolutionary Computation Conference (GECCO-2000), 2000, 8 pages).
Dietterich (“An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization”, Machine Learning, Kluwer Academic Publisher, Boston, 1999, pp. 1-22).
Dietterich, T.G., “An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting and Randomization,” Machine Learning, Kluwer Academic Publishers, Boston, (1999), pp. 1-22.
Miller, B. L., et al., “Optimal Sampling for Genetic Algorithms,” ASME, (1996), pp. 1-6.
Cantu-Paz, E., et al., “Using Evolutionary Algorithms to Induce Oblique Decision Trees,” Lawrence Livermore National Laboratory, UCRL-JC-137202 Preprint, submitted to Genetic and Evolutionary Computation Conference, Las Vegas, NV, July 8-12, 2000, Jan. 21, 2000, 12 pages.
Cantu-Paz, E., et al., “Using Evolutionary Algorithms to Induce Oblique Decision Trees,” Lawrence Livermore National Laboratory, accepted for publication in the Genetic and Evolutionary Computation Conference (GECCO-2000), (2002), 8 pages.

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