从流图中学习决策规则

NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society Pub Date : 2007-06-24 DOI:10.1109/NAFIPS.2007.383918

Chien-Chung Chan, S. Tsumoto

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引用次数: 5

摘要

利用流图来表示数据表中的信息流分布，用于智能数据分析是由Pawlak首先提出的。本文研究了用多集决策表表示流图的方法。这种表示是最小的。在流图的启发下，给出了一种新的基于此表示的规则学习算法。从特定的例子和边界集中的例子中学习两套规则。规则由Pawlak引入的贝叶斯因子表征。

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On Learning Decision Rules From Flow Graphs

The use of flow graphs to represent information flow distribution from data tables for intelligent data analysis was first proposed by Pawlak. This paper studies the representation of flow graphs by multiset decision tables. This representation is minimal. Inspired by the flow graphs, a new rule learning algorithm based on this representation is presented with examples. Two sets of rules are learned from certain examples and examples in the boundary set. Rules are characterized by Bayesian factors introduced by Pawlak.

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NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society

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