A novel text classification approach based on cloud concept jumping up

作者：Dai Jin^*; He Zhongshi; Feng Yinyin

来源：Journal of Computational Information Systems, 2011, 7(9): 3275-3283.

• Classification performance
• Cloud models
• Concept jumping up
• Natural languages
• On-machines
• Quantitative data
• Text classification
• Text classifiers
• Text cloud similarity
• Text feature
• Text information
• Text mining

The uncertainty of natural language is the most important problem in text mining based on machine learning. In order to reduce the interference of this uncertainty to text classification, a novel text classifier based on cloud concept jumping up (CCJU) is proposed. Based on cloud model theory, it can efficiently accomplish conversion between qualitative concept and quantitative data. Through the conversion from text set to text information table based on VSM model, the text qualitative concept, which is extraction from the same category, is jumping up as a whole category concept. According to compare the cloud similarity between the test text and each category concept, the test text is assigned to the most similar category. By the comparison among different text classifiers in different feature selection set, it full proves CCJU not only has a strong ability to adapt to the different text features, the classification performance is also better than the traditional classifiers.

• 出版日期2011