Chris Bourke

Learn More
We study the problem of optimizing a multi-class classifier based on its ROC hypersur-face and a matrix describing the costs of each type of prediction error. For a binary classi-fier, it is straightforward to find an optimal operating point based on its ROC curve and the relative cost of true positive to false positive error. However, the corresponding(More)
We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ class labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which(More)
  • 1