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DU Fanghua, JI Junzhong, ZHAO Xuewu, WU Chensheng. Semi-supervised Text Classification Algorithm Based on a Feature Mapping[J]. Journal of Beijing University of Technology, 2016, 42(2): 230-235. DOI: 10.11936/bjutxb2015040087
Citation: DU Fanghua, JI Junzhong, ZHAO Xuewu, WU Chensheng. Semi-supervised Text Classification Algorithm Based on a Feature Mapping[J]. Journal of Beijing University of Technology, 2016, 42(2): 230-235. DOI: 10.11936/bjutxb2015040087

Semi-supervised Text Classification Algorithm Based on a Feature Mapping

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  • Received Date: April 28, 2015
  • Available Online: January 10, 2023
  • There are many algorithms based on data distribution to effectively solve the problem of semisupervised text categorization.However,they may perform badly when the labeled data distribution is different from the unlabeled data.To solve the problem,semi-supervised text classification algorithm based on feature mapping was proposed.First,three sets of features were selected respectively from labeled data,unlabeled data and test data by using different feature selection methods,and their values were initialize.Second,three feature mapping functions were studied,and the weight of each feature was recalculated by them.Finally,the EM algorithm was employ to classify the text data.Experiments of standard data sets show that the proposed algorithm is effective.
  • [1]
    苏金树,张博锋,徐昕.基于机器学习的文本分类技术研究进展[J].软件学报,2006,17(9):1848-1859.SU J S,ZHANG B F,XU X.Advances in machine learning based text categorization[J].Journal of Software,2006,17(9):1848-1859.(in Chinese)
    [2]
    ZHU X,GOLDBERG A B.Introduction to semisupervised learning[J].Synthesis Lectures on Artificial Intelligence and Machine Learning,2009,3(1):1-130.
    [3]
    周志华,王珏.机器学习及其应用[M].北京:清华大学出版社,2007:259-275.
    [4]
    PAN S J,YANG Q.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
    [5]
    庄福振,罗平,何清,等.迁移学习研究进展[J].软件学报,2015,26(1):26-39.ZHUANG F Z,LUO P,HE Q,et al.Survey on transfer learning research[J].Journal of Software,2015,26(1):26-39.(in Chinese)
    [6]
    LONG M,WANG J,DING G,et al.Transfer learning with graph co-regularization[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(7):1805-1818.
    [7]
    PAN S J,KWOK J T,YANG Q.Transfer learning via dimensionality reduction[C]//Chicago,Illinois:AAAI,2008:677-682.
    [8]
    PERLICH C,DALESSANDRO B,RAEDER T,et al.Machine learning for targeted display advertising:transfer learning in action[J].Machine Learning,2014,95(1):103-127.
    [9]
    NG M,WU Q,YE Y.Co-transfer learning using coupled Markov chains with restart[J].IEEE Intelligent Systems,2014,29(4):26-33.
    [10]
    YANG L,HANNEKE S,CARBONELL J.A theory of transfer learning with applications to active learning[J].Machine Learning,2013,90(2):161-189.
    [11]
    YANG Q.Big data,lifelong machine learning and transfer learning[C]//Proceedings of the Sixth ACM International Conference on Web Search and Data Mining.New York,NY:ACM,2013:505-506.
    [12]
    DAI W,CHEN Y,XUE G R,et al.Translated learning:transfer learning across different feature spaces[C]//NIPS Proceedings of 21st Annual Conference on Neural Information Precessing System,2008:353-360.
    [13]
    孟佳娜.迁移学习在文本分类中的应用研究[D].大连:大连理工大学,2011.MENG J N.Research on the application of transfer learningon text classification[D].Dalian:Dalian University of Technology,2011.(in Chinese)
    [14]
    JONES K S,WILLETT P.Readings in information retrieval[M].San Francisco;Morgan Kaufmann,1997:231-257.
    [15]
    FXSIJY.结巴中文分词项目[EB/OL].[2013-01-25].http://github.com/fxsjy/jieba.
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