Abstract
In the territory of text categorization, the distribution and quality of sample set is highly influential to categorization result. Associated rule categorization ARC-BC is effective under common circumstances. The accuracy of categorization obviously falls as distribution of feature words of training samples is uneven. In this paper, a Chinese text classification approach was proposed based on sample weighting associated rules (SW-ARC). The approach improved substantial classification efficiency by performing self-adapting sample weights adjustment. Experiment result shows SW-ARC can solve the quality fall caused by uneven distribution of feature words. Macro-average recall of open test increases from 50% of ARC-BC to 70% of SW-ARC, Macro-average precision increases from 28% of ARC-BC to 70% of SW-ARC.
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Zhang, J., Chen, X., Chen, Y., Hu, Y. (2005). Association Classification Based on Sample Weighting. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_77
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DOI: https://doi.org/10.1007/11540007_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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