Method of spam filtering online based on QBC active sampling learning algorithm

Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (22): 170-174.

Previous Articles Next Articles

Method of spam filtering online based on QBC active sampling learning algorithm

CHEN Nian1，2, TANG Zhenmin2

1.Department of Mathematics and Computer Science, Chizhou College, Chizhou, Anhui 247000, China
2.College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Online:2014-11-15 Published:2014-11-13

QBC主动采样学习在垃圾邮件在线过滤中的应用

陈念1，2，唐振民2

1.池州学院数学与计算机科学系，安徽池州 247000
2.南京理工大学计算机科学与工程学院，南京 210094

Abstract

Abstract: A method is put forward in the paper which can get informative samples from unlabeled-sample pool with stepped way. The method which is based on query-by-committee algorithm increases the sampling threshold dynamically and it is in order to solve the problem of spam filtering online. Through the new method, the number of samples which is used for labeling and training is further reduced and the accuracy of classifier can remain stable. By experiments on Spambase datasets, the effectiveness which can improve efficiency of machine learning is certificated.

Key words: spam filtering, version space, active learning, vote entropy, query-by-committee algorithm

摘要： 针对垃圾邮件在线过滤的实际应用，在委员会投票算法采样学习的基础上，提出动态提升采样门槛，在无标签样本池中阶梯式获取高信息量训练样本的方法。该方法能够在稳定识别精度的前提下，进一步降低用于标注和学习的样本数量，压缩由此带来的时间成本。通过在UCI的Spambase数据集上仿真，证明了该方法在改善学习效率方面的有效性。

关键词: 垃圾邮件过滤, 版本空间, 主动学习, 投票熵, 委员会投票算法

CHEN Nian1，2, TANG Zhenmin2. Method of spam filtering online based on QBC active sampling learning algorithm[J]. Computer Engineering and Applications, 2014, 50(22): 170-174.

陈念1，2，唐振民2. QBC主动采样学习在垃圾邮件在线过滤中的应用[J]. 计算机工程与应用, 2014, 50(22): 170-174.

[1]	ZHANG Hainan, YOU Xiaoming, LIU Sheng, LIU Zhongqiang. Interactive Learning Cuckoo Search Algorithm [J]. Computer Engineering and Applications, 2020, 56(7): 147-154.
[2]	ZHAO Xiaoyong, WANG Ningning, WANG Lei. Research of Outlier Ensemble Mining Based on Active Learning [J]. Computer Engineering and Applications, 2020, 56(12): 112-117.
[3]	MA Jianhong, ZHANG Bingfei, ZHANG Shaoguang, LIU Shuangyao. Named Entity Recognition for New Energy Vehicles Based on Active MCNN-SCRF [J]. Computer Engineering and Applications, 2019, 55(7): 23-29.
[4]	YANG Chengwen, LI Jiming, YANG Dongyong. Active Learning for Hyperspectral Image Classification with Deep Bayesian [J]. Computer Engineering and Applications, 2019, 55(18): 166-172.
[5]	ZHAO Yue, LI Yaoqiang, XU Xiaona, WU Licheng. Near-optimal active learning for Tibetan speech recognition [J]. Computer Engineering and Applications, 2018, 54(22): 156-159.
[6]	YAO Qiong1, XU Xiang1，2, ZOU Kun1. 3D Gabor based multi-view active learning for hyperspectral image classification [J]. Computer Engineering and Applications, 2018, 54(22): 197-204.
[7]	CHEN Juan1, ZHU Fuxi1，2. Time series classification based on PU problem with semi-supervised learning and active learning [J]. Computer Engineering and Applications, 2018, 54(11): 116-121.
[8]	ZHAO Pengfei, ZHOU Shaoguang, YI Yang, HU Yiqun. Classification method of hyperspectral remote sensing image based on SLIC and active learning [J]. Computer Engineering and Applications, 2017, 53(3): 183-187.
[9]	CHENG Yuan’e, ZHOU Shaoguang, YUAN Chunqi, CHEN Mengmeng. Hyperspectral image classification based on active deep learning [J]. Computer Engineering and Applications, 2017, 53(17): 192-196.
[10]	XIA Yuguo1, DAI Hongxia1, GU Mingliang2. Chinese dialect active identification method fusing diversity measure [J]. Computer Engineering and Applications, 2017, 53(15): 149-154.
[11]	SHAO Xi, YAO Lei. Music classification based on SVM active learning [J]. Computer Engineering and Applications, 2016, 52(6): 127-133.
[12]	LI Junnan, LV Jia. Ensemble self-training method based on active learning and confidence voting [J]. Computer Engineering and Applications, 2016, 52(20): 167-171.
[13]	XIA Huosong, LUO Pan, GAO Miaomiao. Filtering algorithm for SMS spam based on design science [J]. Computer Engineering and Applications, 2016, 52(19): 111-116.
[14]	QIN Hai, ZHANG Dongbo, WANG Junchao, YAN Shuang. High efficient ensemble learning based on disagreement strategy in data stream environment [J]. Computer Engineering and Applications, 2016, 52(13): 89-94.
[15]	XIONG Zhongyang, ZHOU Xingqin, ZHANG Yufang. Data stream classifier with limited labelled data [J]. Computer Engineering and Applications, 2015, 51(6): 124-128.

Method of spam filtering online based on QBC active sampling learning algorithm

QBC主动采样学习在垃圾邮件在线过滤中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics