Bayesian classification with correlation and inheritance

Authors:
Robin Hanson;John Stutz;Peter Cheeseman
Affiliations:
Sterling Software and NASA Ames Research Center, Moffett Field, CA;NASA and NASA Ames Research Center, Moffett Field, CA;Research Institute for Advanced Computer Science and NASA Ames Research Center, Moffett Field, CA
Venue:
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Year:
1991

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Abstract

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the Auto Class system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. Simpler versions of AutoClass have been applied to many large real data sets, have discovered new independently-verified phenomena, and have been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters though a class hierarchy.