---
title: "Bayesian Classification with Correlation and Inheritance"
authors:
  - "Robin Hanson"
  - "John C. Stutz"
  - "Peter C. Cheeseman"
doi: ""
url: "https://ijcai.org/proceedings/1991/101"
pdf: "https://ijcai.org/proceedings/1991/0101.pdf"
venue: "Proceedings of the Twelfth International Joint Conference on Artificial Intelligence"
year: 1991
pages: "692-697"
track: "Learning Be Knowledge Acquisition"
keywords:
---

# Bayesian Classification with Correlation and Inheritance

Robin Hanson, John C. Stutz, Peter C. Cheeseman

*Proceedings of the Twelfth International Joint Conference on Artificial Intelligence*, pages 692-697.

## 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 AutoClass 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.