adtree this example non limiting adaptation of adtree provides a framework of cost sensitive learning and also applies a cost sensitive ml technique to the problem of record linkage cost sensitive learning additionally assigns a cost factor c i u2208 0 u221e to each example x i y i in the database to quantify the cost of misclassifying x i into a class label other than y i in the standard boosting algorithm the weight distribution over the data space is revised in an iterative manner to reduce the total error rate cost sensitive learning operates in a similar manner but it operates over a space of examples in which the weight distribution has been updated in a biased manner towards examples with higher costs according to the u201c translation theorem u201d the classifier generated in this way will be conceptually the same as the one that explicitly seeks to reduce the accumulated misclassification cost over the data space we will examine three different methods of biasing the training data to account for the business preference for false negatives vs false positives the baseline weight update rule of adtree is w i t 1 u2190 w i t u00b7 u03b5 u2212.