Rohit Kelkar

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Noisy points in training data maybe due to incorrect class labels or erroneous recording of attribute values. These points greatly influence the orientation of the classification boundary. In this paper, we formalize two notions of noisy points: intrusive outliers and hard-to-classify points. We adapt two well-known distance-based notions of outliers in(More)
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