基于Bayes概率边界域的粗集分类方法及其在高频数据中的应用

统计研究 ›› 2010, Vol. 27 ›› Issue (3): 76-82.

基于Bayes概率边界域的粗集分类方法及其在高频数据中的应用

来升强谢邦昌朱建平

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-03-15 发布日期:2010-03-15

Rough Set Classification Using Bayes Probabilistic Boundary and Its Application in High Frequency Data

LAI Sheng-Qiang Shia Ben-Chang ZHU Jian-Ping

Received:1900-01-01 Revised:1900-01-01 Online:2010-03-15 Published:2010-03-15

摘要/Abstract

摘要： 作为一种近似处理的工具，粗集主要用于不确定情况下的决策分析，并且不需要任何事先的数据假定。但当前的主流粗集分类方法仍然需要先经过离散化的步骤，这就损失了数值型变量提供的高质量信息。本文对隶属函数重新加以概率定义，并提出了一种基于Bayes概率边界域的粗集分类技术，比较好的解决了当前粗集方法所面临的数值型属性分类的不适应、分类规则不完备等一系列问题。

关键词: 可变精度粗糙集, Bayes边界域, 高频数据

Abstract: Having been broadly used in decision-making fields Rough Set Theory(RST) provides a way of extracting decision rules without imposing apriori assumptions. However current RST-based classification methods still need to discrete numerical variables into categorical ones, in which potential useful information may be omitted. In this article, we introduce a bayes-based RST classification technique which can solve a series of problems facing with current RST classification, including inability to numerical data, incomplete rule generation and etc.

Key words: Variable Precision Rough Set, Bayes Boundary, High-frequency Data

来升强谢邦昌朱建平. 基于Bayes概率边界域的粗集分类方法及其在高频数据中的应用[J]. 统计研究, 2010, 27(3): 76-82.

LAI Sheng-Qiang Shia Ben-Chang ZHU Jian-Ping. Rough Set Classification Using Bayes Probabilistic Boundary and Its Application in High Frequency Data[J]. Statistical Research, 2010, 27(3): 76-82.

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