Data imbalance and data noise often coexist in real world datasets. Data imbalance affects the learning classifier by degrading the recognition power of the classifier on the minority class, while data noise affects the learning classifier by providing inaccurate information and thus misleads the classifier. Because of these differences, data imbalance and data noise have been treated separately in the data mining field. Yet, such approach ignores the mutual effects and as a result may lead to new problems. A desirable solution is to tackle these two issues jointly.
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- Partial requirement for: Ph.D., Arizona State University, 2014Note typethesis
- Includes bibliographical references (p. 64-72)Note typebibliography
- Field of study: Industrial engineering