In regular classification the aim is to minimize the misclassification rate and thus all types of misclassification errors are deemed equally severe.
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 ...
... sensitive threshold computed under the assumption of knowing the instance's true classification ... Zhao, “Instance Weighting versus Threshold Adjusting for. Cost ...
ROC curves for the instance weighting and threshold adjusting approach for making classifiers cost sensitive. Source publication. Figure 1 ...
Aug 21, 2020 · This modification of SVM that weighs the margin proportional to the class importance is often referred to as weighted SVM, or cost-sensitive SVM.
[PDF] An instance-weighting method to induce cost-sensitive trees
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The Statlog project [5] studied a number of classifiers for cost- sensitive classification in the Heart and GermanCredit data sets. We restate their results ...
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May 14, 2021 · ... classify a new instance into one or more potential classes. ... Score-driven threshold can be adjusted globally (per dataset) or locally (per ...
The method, called Thresholding, selects a proper threshold from training instances according to the misclassification cost. Similar to other cost-sensitive ...
Traditionally, classification algorithms aim to minimize the number of errors. However, this approach can lead to sub-optimal results for the common case.
... adjustment of these scores can be important in terms of F1- measure maximization. The reason is that the decision threshold given by cost-sensitive SVMs or.