Text categorization based on active learning support vector machines

doi:10.3778/j.issn.1002-8331.2009.02.039

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (2): 134-136.DOI: 10.3778/j.issn.1002-8331.2009.02.039

• 数据库、信号与信息处理 • Previous Articles Next Articles

Text categorization based on active learning support vector machines

ZHU Hong-bin¹,CAI Yu²

1.College of Computer and Information Engineering，Lishui University，Lishui，Zhejiang 323000，China
2.Information Center of National Tax Bureau，Lishui，Zhejiang 323000，China

Received:2007-11-09 Revised:2008-02-29 Online:2009-01-11 Published:2009-01-11
Contact: ZHU Hong-bin

基于主动学习支持向量机的文本分类

朱红斌¹,蔡郁²

1.丽水学院计算机与信息工程学院，浙江丽水 323000
2.丽水市国家税务局信息中心，浙江丽水 323000

通讯作者: 朱红斌

Abstract

Abstract: A text categorization method based on active learning support vector machines is proposed in this paper.First vector space model is used to extract text feature and mutual information is used to feature selection.Then active learning method is proposed to train the support vector machines.And SVM is used to classify the un-classification text.Then the evaluation method and results are given.The evaluation results show that the classifier is effective.

Key words: vector space model, active learning, support vector machines, text categorization

摘要： 提出基于主动学习支持向量机的文本分类方法，首先采用向量空间模型（VSM）对文本特征进行提取，使用互信息对文本特征进行降维，然后提出主动学习算法对支持向量机进行训练，使用训练后的分类器对新的文本进行分类，实验结果表明该方法具有良好的分类性能。

关键词: 向量空间模型, 主动学习, 支持向量机, 文本分类

ZHU Hong-bin¹,CAI Yu². Text categorization based on active learning support vector machines[J]. Computer Engineering and Applications, 2009, 45(2): 134-136.

朱红斌¹,蔡郁². 基于主动学习支持向量机的文本分类[J]. 计算机工程与应用, 2009, 45(2): 134-136.

[1]	ZHENG Linwen, ZHOU Jinzhi, HUANG Jing. Application of Deep Sparse Auto-Encoders in ECG Feature Extraction [J]. Computer Engineering and Applications, 2021, 57(11): 156-161.
[2]	SHEN Yanguang, JIA Yaoqing. Text Categorization Method Based on Word Co-occurrence and Graph Convolution [J]. Computer Engineering and Applications, 2021, 57(11): 173-178.
[3]	WEN Tingxin, KONG Xiangbo. Research on Extreme Risk Warning in Financial Market from Imbalance Distribution of Samples [J]. Computer Engineering and Applications, 2020, 56(8): 256-260.
[4]	ZHANG Hainan, YOU Xiaoming, LIU Sheng, LIU Zhongqiang. Interactive Learning Cuckoo Search Algorithm [J]. Computer Engineering and Applications, 2020, 56(7): 147-154.
[5]	HAN Bang, LI Zichen, TANG Yongli. Design and Implementation of Full Text Retrieval Scheme Based on Homomorphic Encryption [J]. Computer Engineering and Applications, 2020, 56(21): 103-107.
[6]	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.
[7]	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.
[8]	PENG Yuan, DUAN Xianhua, WANG Wanyao, LU Wenchao. Multi-Cue Feature Fusion Based Image Classification [J]. Computer Engineering and Applications, 2019, 55(20): 164-169.
[9]	YE Xuemei1，2, MAO Xuemin1，2, XIA Jinchun1，2, WANG Bo1，2. Improved Approach to TF-IDF Algorithm in Text Classification [J]. Computer Engineering and Applications, 2019, 55(2): 104-109.
[10]	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.
[11]	HE Li, LIU Ying, HAN Keping. Improved TSVM Learning Algorithm Under Noise Labeling [J]. Computer Engineering and Applications, 2019, 55(17): 44-50.
[12]	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.
[13]	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.
[14]	XIANG Guangli, LI Ankang, LIN Xiang, XIONG Bin. Multiple keywords retrieval scheme based on homomorphic encryption [J]. Computer Engineering and Applications, 2018, 54(2): 97-101.
[15]	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.

Text categorization based on active learning support vector machines

基于主动学习支持向量机的文本分类

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics