Abstract
In this paper document classification methods using multinomial naïve Bayes are improved in a number of ways. We use the value weighting method, a new fine-grained weighting method, to calculate the weights of the feature values. Our experiments show that the proposed approach outperforms other state-of-the-art methods.
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© 2015 Springer International Publishing Switzerland
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Song, SH., Lee, CH. (2015). Improving Document Classification Using Fine-Grained Weights. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_47
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DOI: https://doi.org/10.1007/978-3-319-19066-2_47
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-19066-2
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