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| An Improved TF-IDF Feature Selection Based on Categorical Description |
| Xu Dongdong, Wu Shaobo |
| School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China |
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Abstract [Objective] Improve the text categorization accuracy by modifying the weighting approach in feature selection. [Methods] Introducing the inner and outer categorical information, and modifying the TF-IDF weighting, this paper proposes the TF-IDF-CD approach which based on the categorical description. Combining TF-IDF-CD with varied classifiers, such as NB and SVM, this paper conducts text categorization experiment in balanced corpus and unbalanced corpus respectively. At last, the accuracies of different weighting approaches are compared with TF-IDF-CD. [Results] The TF-IDF-CD performs well even when there are a less number of feature items. Compared to the TF-IDF, when combined with varied classifiers in different corpus, the TF-IDF-CD can greatly improve the average accuracies. The minimum increase is 14%, and the maximum up to 30%. Compared to the CTD approach, when combined with NB, SVM, and DT, the TF-IDF-CD could improve the the average accuracy of TC from 1% to 13%. But, in unbalanced corpus, when combined with KNN, the performance of the TF-IDF-CD is 2% lower than CTD. [Limitations] Combined with KNN classifier which is sensitive to the skew data, the TF-IDF-CD needs to be improved to resist the skew characteristics of unbalanced corpus. [Conclusions] Experiment resualts show that the TF-IDF-CD approach is effective.
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Received: 23 August 2014
Published: 16 April 2015
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