Incorporating prior domain knowledge into a kernel based feature selection algorithm

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Advances in Knowledge Discovery and Data Mining, 11th Pacific-Asia Conference Proceedings, 2007, pp. 1064 - 1071
Issue Date:
2007-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2007001614.pdf635.99 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: