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The quality of data is an important issue in any domain-specific data mining and knowledge discovery initiative. The validity of solutions produced by data-driven algorithms can be diminished if the data being analyzed are of low quality. The quality of data is often realized in terms of data noise present in the given dataset and can include noisy attributes or labeling errors. Hence, tools for improving...
Erroneous attribute values can significantly impact learning from otherwise valuable data. The learning impact can be exacerbated by the class imbalanced training data. We investigate and compare the overall learning impact of sampling such data by using four distinct performance metrics suitable for models built from binary class imbalanced data. Seven relatively free of noise, class imbalanced software...
Missing values are commonly encountered in software measurement data, and k nearest neighbor imputation (kNNI) is one of the most popular imputation procedures used by researchers and practitioners in empirical software engineering. Imputation techniques are used to replace missing values with one or more alternatives. Traditionally, kNNI uses only complete cases as possible donors for imputation...
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