Classification technique using random decision forests

Details Classification technique using random decision forests Classification technique using random decision forests

1999-01-14

1999-12-28

Boudreau, Leo H.

Image analysis

Pattern recognition

Classification

382226, 382227, G06K 962, G06K 968, G06K 970

Patent

active

060091991

ABSTRACT:
In a decision-forest classifier in accordance with the invention, a decision forest including multiple decision trees is used to classify "seen" training data and "unseen" data. Each individual tree performs an initial classification based on randomly selected subsets of the data. The classification outcomes by the individual trees are combined using a discriminant process in the decision-forest classier to render the ultimate classification decision.

REFERENCES:
patent: 5661820 (1997-08-01), Kegelmeyer, Jr.
S. Shlien, "Multiple Binary Decision Tree Classifiers," Pattern Recognition, vol. 23, No. 7, 1990 pp. 757-763.
S. Shlien, "Nonparametric Classification Using Matched Binary Decision Trees," Pattern Recognition Letters, Feb. 13, 1992, pp. 83-87.
S. Kwok et al., "Multiple Decision Trees," Uncertainty in Artificial Intelligence, 4, 1990, pp. 327-335.
L. Bottou et al., "comparison of Classifier Methods: A Case Study in Handwritten Digit Recognition," Proceedings of the 12th International Conference on Pattern Recognition, II, Jerusalem, Israel, Oct. 9-13, 1994 pp. 77-82.
L. Hansen et al., "Neural Network Ensembles,"IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 12, No. 10, Oct. 1990, pp. 993-1001.
D. Heath et al., "Induction of Oblique Decision Trees," Proceedings of the 13th International Joint Conference on Artificial Intelligence, 2, Chamber, France, Aug. 28-Sep. 3, 1993, pp. 1002-1007.
T. Ho, "Recognition of Handwritten Digits by Combining Independent Learning Vector Quantizations," Proceedings of the Second International Conference on Document Analysis and Recognition, Tsukuba Science City, Japan, Oct. 20-22, 1993, pp. 818-821.
M. Jordan et al., "Hierarchical Mixtures of Experts and the EM Algorithm," A.I. Memo No. 1440, C.B.C.L. Memo No. 83, MIT Artificial Intelligence Laboratory, Center for Biological and Computational Learning, and Department of Brain and Cognitive Sciences, Aug. 6, 1993.
E. Kleinberg, "Stochastic Discrimination," Annuals of Mathematics and Artificial Intelligence, 1, 1990, pp. 207-239.
E. Kleinberg et al., "Pattern Recognition by Stochastic Modeling," Proceedings of the Third International Workshop on Frontiers in Handwriting Recogniton, Buffalo, May 1993, pp. 175-183.
Y. Park, "A Comparison of Neural Net Classifiers and Linear Tree Classifiers: Their Similarities and Differences," Pattern Recognition, vol. 27, No. 11, 1994, pp. 1493-1503.
J. Schuermann, "A Multifont Word Recognition system for Postal Address Reading," IEEE Transactions on Computers, C-27, No. 8, Aug. 1978, pp. 721-732.
J. Schuermann et al., "A Decision Theoretic Approach to Hierarchical Classifier Design," Pattern Recognition, vol. 17, No. 3, 1984, pp. 359-369.
I Sethi et al., "Hierarchical Classifier Design Using Mutual Information," IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4, No. 4, Jul. 1982, pp. 441-445.
S. Shlien, "Multiple Binary Decision Tree Classifiers," Pattern Recognition, vol. 23, No. 7, 1990, pp. 757-763.

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