Hybrid sampling for imbalanced data
Integrated Computer-Aided Engineering - Selected papers from the IEEE Conference on Information Reuse and Integration (IRI), July 13-15, 2008
Knowledge discovery from imbalanced and noisy data
Data & Knowledge Engineering
IEEE Transactions on Neural Networks
Predicting high-risk program modules by selecting the right software measurements
Software Quality Control
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A practical problem in data mining and machine learning is the limited availability of data. For example, in a binary classification problem it is often the case that examples of one class are abundant, while examples of the other class are in short supply. Examples from one class, typically the positive class, can be limited due to the financial cost or time required to collect these examples. This work presents a comprehensive empirical study of learning when examples from one class are extremely rare, but examples of the other class(es) are plentiful. Specifically, we address the issue of how many examples from the abundant class should be used when training a classifier on data where one class is very rare. Nearly one million classifiers were built and evaluated to generate the results presented in this work. Our results demonstrate that the often used `even distribution' is not optimal when dealing with such rare events.