A Modified Bayes Classifier Using Association Rule Forest

吴宁; 柏春霞; 祝毅博

您当前的位置：

首页 >

文章列表页 >

A Modified Bayes Classifier Using Association Rule Forest

更新时间：2025-12-08

- A Modified Bayes Classifier Using Association Rule Forest
- Vol. 43, Issue 2, Pages: 48-52(2009)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP311.13
- Online First：10 February 2009，
  
  Published：2009
- 稿件说明：
移动端阅览
吴宁, 柏春霞, 祝毅博. A Modified Bayes Classifier Using Association Rule Forest[J]. 2009, 43(2): 48-52.
DOI：

吴宁, 柏春霞, 祝毅博. A Modified Bayes Classifier Using Association Rule Forest[J]. 2009, 43(2): 48-52. DOI：

摘要

针对朴素贝叶斯分类方法中属性值条件独立假设不适应实际情况的问题

提出了关联规则森林表示法及应用关联规则森林的改进贝叶斯分类算法(ABC算法).ABC算法利用关联规则挖掘得到满足条件的关联规则

并由此来构造关联规则森林

而规则森林中所有根节点的概率与所有适用的规则置信度连乘

就得到所有属性值的联合概率.应用UDI数据集对分类器进行了测试

分类结果表明

ABC算法的分类准确率明显高于朴素贝叶斯分类算法

平均提高5%

特别是对属性间有着较强依赖关系的数据集

其分类准确率提高了37%.

Abstract

To alleviate the independent assumption on the attribute of the naive Bayes classification

an association rule forest representation and a modified Bayes classifier called ABC are proposed. A data mining method is used to get useful association rules. An association rule forest is constructed from all the resulting useful association rules. Then the joint probability of all the attributes contained in an instance is calculated by multiplying the probabilities of all root nodes with the confidences of all the useful association rules. UDI dataset is used to verify the validation of the ABC. Experimental results show that the ABC has higher classification accuracy

with 5% average improvement

than the naive Bayes one has. Especially

for the dataset containing strong associated attributes

37% improvement in accuracy is obtained.

关键词

Keywords

references

HAN Jiawei, KAMBER M. 数据挖掘概念与技术[M].范明,孟小峰,译. 北京: 机械工业出版社, 2006.

FRIEDMAN N, GEIGER D, GOLDSZMIT M. Bayesian network classifier[J]. Machine Learning, 1997, 29: 131-163.

KEOGH E J, PAZZANNI M J. Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches[C]∥Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics. San Francisco: Morgan Kaufmann Publishers, 1999: 225-230.

CHICKERING D M, GEIGER D, HECKERMAN D. Learning Bayesian networks is NP-complete[M]∥Learning from Data: Artificial Intelligence and Statistics. Berlin, Germany: Springer-Verlag, 1996: 121-130.

WEBB G I, BOUGHTON J R, WANG Zhihai. Not so naive Bayes: aggregating one-dependence estimators [J]. Machine Learning, 2005, 58(1): 5-24.

MARTINEZ M, SUCAR L E. Learning an optimal naive Bayes classifier [C]∥Proceedings of International Conference on Pattern Recognition. Las Alamitos, CA, USA: IEEE Computer Society Press, 2006: 1236-1239.

石洪波,王志海,黄厚宽,等. 一种限定性的双层贝叶斯分类模型[J]. 软件学报, 2004, 15(2): 193-199.

SHI Hongbo, WANG Zhihai, HUANG Houkuan, et al. A restricted double-level Bayesian classification model [J]. Journal of Software, 2004, 15(2): 193-199.

MERETAKIS D, WUTHRICH B. Extending naive Bayes classification using long itemsets [C]∥Proceedings of 5th ACM SIGKDD International Conf on Knowledge Discovery and Data Mining.New York, USA: ACM Press,1999:165-174.

ABELLÁN J, CANO A, MASEGOSA A R, et al. A semi-naive Bayes classifier with grouping of cases [C]∥Proceedings of European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty. Berlin, Germany: Springer-Verlay, 2007: 477-488.

眭俊明, 姜远, 周志华. 基于频繁项集挖掘的贝叶斯分类算法[J]. 计算机研究与发展, 2007, 44(8):1293-1300.

XU Junming, JIANG Yuan, ZHOU Zhihua. Bayesian classifier based on frequent item sets mining [J]. Journal of Computer Research and Development, 2007, 44(8):1293-1300.

HAN Jiawei, PEI Jian, YIN Yiwen, et al. Mining frequent patterns without candidate generation: a frequent-pattern tree approach[J]. Data Mining and Knowledge Discovery, 2004, 8(1): 53-87.

何军,刘红岩,杜小勇. 挖掘多关系关联规则[J]. 软件学报, 2007,18(11): 2752-2765.

HE Jun, LIU Hongyan, DU Xiaoyong. Mining of multi-relational association rules [J]. Journal of Software, 2007, 18(11): 2752-2765.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

An Associative Classification Algorithm by Distilling Effective Rules

Associative Rule-Based Text Categorization Method Using Category Similarity

Two New Efficient Algorithms to User Access Prediction

Extracting Association Rules in Three-Way Concept Lattices

Numerical Simulation of Heat-Moisture Barrier Performance of a Horizontal Air Curtain in Cold Storage

Related Author

方加沛 2

沈钧毅 1

武建华 1

杨攀 1

郭岳龙 1

Related Institution

Department of Computer Science, Zhuhai Collage, Jinan University

Shaanxi Province Key Laboratory of Computer Network, Xi'an Jiaotong University

School of Information Science and Technology, Xiamen University

School of Computer Science and Technology, Xidian University

⁰