MetaCost: A General Method for Making Classifiers Cost-Sensitive MetaCost: A General Method for Making Classifiers Cost-Sensitive
Paper summary MetaCost is a meta-algorithm which makes error-based classifiers making their decision based on the cost of errors. For example, sending advertisement is cheap, so it might be worth a lot of false positives to get a single person who is actually interested in the advertisement. The algorithm is given in pseudocode in the paper. Important notation: * $C(i, j)$: Cost of predicting an example belongs to class $i$, where in fact it belongs to class $j$.
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MetaCost: A General Method for Making Classifiers Cost-Sensitive
Domingos, Pedro M.
ACM KDD - 1999 via Bibsonomy
Keywords: dblp


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