document: Quantification and semi-supervised classification methods for handling changes in class distribution In realistic settings the prevalence of a class may change after a classifier is induced and this will degrade the performance of the classifier . Further complicating this scenario is the fact that labeled data is often scarce and expensive . In this paper we address the problem where the class distribution changes and only unlabeled examples are available from the new distribution . We design and evaluate a number of methods for coping with this problem and compare the performance of these methods . Our quantification-based methods estimate the class distribution of the unlabeled data from the changed distribution and adjust the original classifier accordingly , while our semi-supervised methods build a new classifier using the examples from the new ( unlabeled ) distribution which are supplemented with predicted class values . We also introduce a hybrid method that utilizes both quantification and semi-supervised learning . All methods are evaluated using accuracy and F-measure on a set of benchmark data sets . Our results demonstrate that our methods yield substantial improvements in accuracy and F-measure .
doc_bio_tags: B O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O