In this paper we investigate the use of causal and non-causal feature selection methods for linear classifiers in situations where the causal relationships between the input and response variables may differ between the training and operational data. The causal feature selection methods investigated include inference of the Markov Blanket and inference of direct causes and of direct effects. The non-causal feature selection method is based on logistic regression with Bayesian regularisation using a Laplace prior. A simple ridge regression model is used as the base classifier, where the ridge parameter is efficiently tuned so as to minimise the leave-one-out error, via eigen-decomposition of the data covariance matrix. For tasks with more features than patterns, linear kernel ridge regression is used for computational efficiency. Results are presented for all of the WCCI-2008 Causation and Prediction Challenge datasets, demonstrating that, somewhat surprisingly, causal feature selection procedures do not provide significant benefits in terms of predictive accuracy over non-causal feature selection and/or classification using the entire feature set.
|Number of pages||22|
|Publication status||Published - 2009|
|Event||Journal of Machine Learning Research: Workshop and Conference Proceedings - Hong Kong|
Duration: 1 Jun 2008 → 6 Jun 2008
|Conference||Journal of Machine Learning Research: Workshop and Conference Proceedings|
|Period||1/06/08 → 6/06/08|