Classification using probability estimate re-sampling

Details Classification using probability estimate re-sampling Classification using probability estimate re-sampling

2007-03-20

2007-03-20

Barlow, John (Department: 2863)

Data processing: measuring, calibrating, or testing

Measurement system

Statistical measurement

Reexamination Certificate

active

10547014

ABSTRACT:
Embodiments of a computer-implemented method of calculating estimates of a joint posterior probability of class membership given combinations of attribute values of a pair of attributes are disclosed. The calculating is performed on the basis of data representing a training set of a plurality of instances defined by attribute values for a plurality of attributes together with a class membership outcome. Embodiments of the method comprise calculating first and second estimates of a posterior probability of class membership given attribute values of a first and second attribute of the pair, respectively, and binning the first and second estimates into a respective plurality of first and second probability range bins. Instances of the training set are mapped to combinations of one of each of the first and second pluralities of probability range bins, and on the basis of the mapping, calculating estimates of a joint posterior probability of class membership.

REFERENCES:
patent: WO 00/67194 (2000-11-01), None
Breiman, Leo, “Bagging Predictors”, Machine Learning, vol. 24, No. 2, Aug. 31, 1996, pp. 123-140, Kluwer Academic Publishers, Netherlands.
Mitchell, Tom M., Machine Learning, Chapter 3, 1997, McGraw-Hill, pp. 53-72.
Mitchell, Tom M., Machine Learning, Chapter 6, 1997, McGraw-Hill, pp. 154-200.

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