Instance weighting and post hoc threshold adjusting are two major approaches to cost-sensitive classifier learning. This paper compares the effects of these two ...
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What is thresholding for making classifiers cost-sensitive?
What is cost-sensitive classification?
Abstract In real-world classification problems, different types of misclassification errors often have asymmetric costs, thus demanding cost-sensitive ...
May 10, 2006 · Abstract In real-world classification problems, different types of misclassification errors often have asymmetric costs, thus demanding ...
Jul 15, 2020 · Is it a good idea to oversample/downsample the data and then use the weight only to control the miss-classification cost? Or would it be better ...
Missing: Instance versus
The method, called Thresholding, selects a proper threshold from training instances according to the misclassification cost. Similar to other cost-sensitive ...
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Jan 14, 2020 · In imbalanced classification, data resampling refers to techniques that transform the training dataset to better balance the class distribution.
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Given a cost matrix of this type, theoretical thresholds and weights can be derived in a similar manner as in the binary case. Obviously, the cost matrix given ...
Aug 21, 2020 · Our intuition for cost-sensitive tree induction is to modify the weight of an instance proportional to the cost of misclassifying the class to ...
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An Instance-Weighting Method to Induce Cost-Sensitive Trees
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The algorithm incorporating the instance-weighting method is found to be better than the original algorithm in terms of total misclassification costs, ...
For a binary classifier, the default threshold is defined as a posterior probability estimate of 0.5 or a decision score of 0.0. However, this default strategy ...