Abstract
This paper proposes a framework for exploiting hierarchical structures of feature domain values in order to improve classification performance under Bayesian learning framework. Inspired by the statistical technique called shrinkage, we investigate the variances in the estimation of parameters for Bayesian learning. We develop two algorithms by maintaining a balance between precision and robustness to improve the estimation. We have evaluated our methods using two real-world data sets, namely, a weather data set and a yeast gene data set. The results demonstrate that our models benefit from exploring the hierarchical structures.
The work described in this paper was partially supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Nos: CUHK 4385/99E and CUHK 4187/01E)
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Han, Y., Lam, W. (2003). Exploiting Hierarchical Domain Values for Bayesian Learning. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_25
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DOI: https://doi.org/10.1007/3-540-36175-8_25
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