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The Nearest Neighbor rule is one of the most successful classifiers in machine learning. However, it is very sensitive to noisy, redundant and irrelevant features, which may cause its performance to deteriorate. Feature weighting methods try to overcome this problem by incorporating weights into the similarity function to increase or reduce the importance of each feature, according to how they behave...
Classifier performance, particularly of instance-based learners such as k-nearest neighbors, is affected by the presence of noisy data. Noise filters are traditionally employed to remove these corrupted data and improve the classification performance. However, their efficacy depends on the properties of the data, which can be analyzed by what are known as data complexity measures. This paper studies...
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