Abstract
In many classification or regression problems, there may be a lot of irrelevant features. Bayesian automatic relevance determination (ARD) is a popular approach to feature selection. However, the application area of this approach has been limited. In this paper, this approach is utilized in a more general case and it is applied to a binary classification problem with binary features. Also, a new binary classification model and a learning algorithm that can purge unwanted features from the model have been developed.
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