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In recent years, the use of machine learning algorithms (classifiers) has proven to be of great value in solving a variety of problems in software engineering including software faults prediction. This paper extends the idea of predicting software faults by using an ensemble of classifiers which has been shown to improve classification performance in other research fields. Benchmarking results on...
It has been observed that classification in imbalanced data sets have drawn more attention to researchers in knowledge discovery and data mining fields. In such problems, almost all the samples are labeled as one class, while far fewer samples are labeled as the other class, which are usually more important. But traditional classifiers that try to pursue whole accurate performance over a full range...
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