Abstract
This paper proposes a new method of incorporating prior domain knowledge into a kernel based feature selection algorithm. The proposed feature selection algorithm combines the Fast Correlation-Based Filter (FCBF) and the kernel methods in order to uncover an optimal subset of features for the support vector regression. In the proposed algorithm, the Kernel Canonical Correlation Analysis (KCCA) is employed as a measurement of mutual information between feature candidates. Domain knowledge in forms of constraints is used to guide the tuning of the KCCA. In the second experiments, the audit quality research carried by Yang Li and Donald Stokes [1] provides the domain knowledge, and the result extends the original subset of features.
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© 2007 Springer Berlin Heidelberg
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Yu, T., Simoff, S.J., Stokes, D. (2007). Incorporating Prior Domain Knowledge into a Kernel Based Feature Selection Algorithm. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_120
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DOI: https://doi.org/10.1007/978-3-540-71701-0_120
Publisher Name: Springer, Berlin, Heidelberg
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