1. An Ensemble Classification Algorithm for Text Data Stream based on Feature Selection and Topic Model
- Author
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Zhongxin Wang, Jia Zhao, Gang Sun, Liu Jianqiao, Zhengqi Ding, and Xiaowen Guan
- Subjects
Data stream ,Topic model ,0209 industrial biotechnology ,Computer science ,Feature extraction ,Feature selection ,02 engineering and technology ,Mutual information ,Data modeling ,Statistical classification ,ComputingMethodologies_PATTERNRECOGNITION ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Algorithm - Abstract
How to mine valuable information that users are interested in from a continuous text data stream, text data stream classification has received widespread attention as a core technology to solve the problem. This paper proposes a text data stream ensemble classification algorithm that combines feature selection and topic model. Firstly, the mutual information feature selection method is used to remove features that are not related to classification. Secondly, the LDA topic model is used to establish the document-topic distribution. Finally, the pre-processed text data stream is classified by an ensemble classification model. The experimental results show that the proposed text data stream ensemble classification algorithm can improve the classification performance of text data stream.
- Published
- 2020