Feature selection with RVM and its application to prediction modeling

Authors:
Dingfang Li;Wenchao Hu
Affiliations:
School of Mathematics and Statistics, Wuhan University, Wuhan, P.R. China;School of Mathematics and Statistics, Wuhan University, Wuhan, P.R. China
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 4
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Abstract

We describe here a method named FSRVM-PLS for model construction using relevance vector machine (RVM). The most compelling feature of FSRVM-PLS is that it's not necessary to estimate parameters in the feature selection phase benefiting from a fully probabilistic framework. After evaluating the effectiveness of FSRVM on a synthetic data set, our method is applied successfully to the prediction of aqueous solubility and permeability.