This course material presents approaches for the consideration of misclassification costs in supervised learning. The baseline method is the one for which we do not take into account the costs.
Two issues are studied : the metric used for the evaluation of the classifier when a misclassification cost matrix is provided i.e. the expected cost of misclassification (ECM); some approaches which enable to guide the machine learning algorithm towards the minimization of the ECM.
Keywords: cost matrix, misclassification, expected cost of misclassification, bagging, metacost, multicost
Slides: Cost Sensitive Learning
References:
Tanagra Tutorial, "Cost-senstive learning - Comparison of tools", March 2009.
Tanagra Tutorial, "Cost-sensitive decision tree", November 2008.
