Abstract
This paper proposes a framework for handling heterogeneous features containing hierarchical values and texts under Bayesian learning. To exploit hierarchical features, we make use of a statistical technique called shrinkage. We also explore an approach for utilizing text data to improve classification performance. We have evaluated our framework using a yeast gene data set which contain hierarchical features as well as text data.
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 Heterogeneous Features for Classification Learning. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_25
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DOI: https://doi.org/10.1007/978-3-540-45080-1_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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