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Class label noise is a data-level difficulty associated with training objects with incorrectly assigned labels. This problem may originate from poorly documented historic data, errors during data generation process or mistakes made by human experts. Inclusion of such examples during the training process will mislead the classifier by presenting a falsified class distribution and consequently lead...
Imbalanced classification is one of the most challenging machine learning problem. Recent studies show, that often the uneven ratio of objects in classes is not the biggest factor, determining the drop of classification accuracy. It is also related to some difficulties embedded in the nature of the data. In this paper we study the different types of minority class examples and distinguish four groups...
This paper introduces a novel method for forming efficient one-class classifier ensembles. A common problem in one-class classification is a complex structure of the target class, which often leads to creation of a too expanded decision boundary. We propose to employ a clustering step in order to partition the target class into atomic subsets and using these as input for one-class classifiers. By...
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