Improved NB Algorithm Research in Spam Filtering Technology

LIU Yue-feng; YUAN Jiang-hao; ZHANG Xiao-lin

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Improved NB Algorithm Research in Spam Filtering Technology

更新时间：2026-09-04

- Improved NB Algorithm Research in Spam Filtering Technology
- Vol. 34, Issue 4, Pages: 115-120(2017)
- 作者机构：
  
  内蒙古科技大学机构信息工程学院,内蒙古,包头,014000
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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刘月峰, 苑江浩, 张晓琳. 改进NB算法在垃圾邮件过滤技术中的研究[J]. 微电子学与计算机, 2017, 34(4): 115-120.

LIU Yue-feng, YUAN Jiang-hao, ZHANG Xiao-lin. Improved NB Algorithm Research in Spam Filtering Technology[J]. 2017, 34(4): 115-120.
刘月峰, 苑江浩, 张晓琳. 改进NB算法在垃圾邮件过滤技术中的研究[J]. 微电子学与计算机, 2017, 34(4): 115-120. DOI：

LIU Yue-feng, YUAN Jiang-hao, ZHANG Xiao-lin. Improved NB Algorithm Research in Spam Filtering Technology[J]. 2017, 34(4): 115-120. DOI：

摘要

朴素贝叶斯(NB)是一种简单高效的分类算法

且在垃圾邮件过滤中得到广泛应用

但是其属性间独立性的假设在一定程度上影响了分类效果.针对这一问题

提出一种改进的NB算法——FOA-NB算法.该算法将NB算法与果蝇优化算法(FOA)相结合

根据不同特征属性对分类的影响程度赋予不同的权值

通过FOA对权值进行优化

得到全局最优特征权向量

该算法在保留NB算法的简洁高效的优点的同时

通过权值优化获取更加具有决策性的特征属性

从而提高垃圾邮件过滤的正确率和召回率.通过仿真实验与NB算法、加权贝叶斯(WB)进行对比

结果表明FOA-NB算法使得垃圾邮件过滤效果得到明显改善

正确率和召回率均有所提高

且提高幅度约为5%.

Abstract

Naive Bayes (NB) is a simple and efficient classification algorithm

and it is widely used in spam filtering

but because of the independence between the attributes of the hypothesis which has to some extent affected in classification effect. To solve this problem

the FOA-NB algorithm is proposed which is an improved NB algorithm. The algorithm of NB algorithm and Fruit fly optimization algorithm (FOA) combination

according to the different feature attributes of the influence degree of the classification given different weights

to optimize the weights by FOA and get the global optimal feature weight vector

the algorithm in the NB algorithm retains the advantage of simple and efficient at the same time

by optimization of the weights to obtain attributes which have more decision-making

so as to improve the spam filtering correct rate and recall rate. Through the simulation experiment with NB algorithm

Weighted Bayesian (WB)

the results show that the FOA-NB algorithm makes the spam filtering effect has been improved significantly

and the correct rate and recall rate are improved

and the increase of about 5%.

关键词

Keywords

references

计宏. 改进贝叶斯垃圾邮件过滤技术的研究[J]. 计算机测量与控制, 2013, 21(8): 2181-2184. DOI: 2181

潘文超. 果蝇最佳化演算法[M]. 中国台北: 沧海书局, 2011: 10-12. DOI: 10

翟军昌, 秦玉平, 王春立. 改进的朴素贝叶斯垃圾邮件过滤算法[J]. 计算机工程与应用, 2009, 45(14): 145-148. DOI: 145

邓维斌, 王国胤, 王燕. 基于Rough Set的加权朴素贝叶斯分类算法[J]. 计算机科学, 2007, 34(2): 204-219. DOI: 204

Jiang L, Cai Z, Zhang H, et al. Naive Bayes text classifiers: a locally weighted learning approach[J]. Journal of Experimental % Theoretical Artificial Intelligence, 2013, 25(2): 273-286. DOI: 273

Jia Wu, Pan Shirui, Zhu Xingquan, et al. Self-adaptive attribute weighting for Native Bayes classification[J]. Expert Systems with Application. 2015, 42(5): 1487-1502. DOI: 1487

Chang-Hwan Lee. A gradient approach for value weighted classification learning in native Bayes[J]. Knowledge-Based Systems, 2015, 85(3): 71-79. DOI: 71

Pan W T. A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example[J]. Knowledge-Based Systems, 2012, 26(2): 69-74. DOI: 69

Zhang H, Sheng S. Learning weighted native bayes with accurate ranking[C] //Proceedings of the Fourth IEEE International Conference on Data Ming. IEEE Computer Society, 2004: 567-570.

韩俊英, 刘成忠. 自适应混沌果蝇优化算法[J]. 计算机应用, 2013, 33(5): 1313-1316. DOI: 1313

吴小文, 李擎. 果蝇算法和5种群智能算法的寻优性能研究[J]. 火力与指挥控制, 2013, 38(4): 17-25. DOI: 17

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