Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Kernel independent component analysis
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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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.