Krystyna Napierala

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In this paper we studied re-sampling methods for learning classifiers from imbalanced data. We carried out a series of experiments on artificial data sets to explore the impact of noisy and borderline examples from the minority class on the classifier performance. Results showed that if data was sufficiently disturbed by these factors, then the focused(More)
In this paper we consider induction of rule-based classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining majority classes. The minority class is usually of primary interest. However, most rule-based classifiers are biased towards the majority classes and they have difficulties with correct(More)
Many real-world applications reveal difficulties in learning classifiers from imbalanced data. Although several methods for improving classifiers have been introduced, the identification of conditions for the efficient use of the particular method is still an open research problem. It is also worth to study the nature of imbalanced data, characteristics of(More)
In a number of practical scenarios a wireless device needs to mark its presence, for instance, to some access point. That enables the access point to assign the device its transmission slot or update the count of the network nodes. Many protocols can achieve exactly this result. In this paper, our goal is to show how that can be done in the simplest(More)