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k-nearest neighbors (k-NN) voting rules are an effective tool in countless many machine learning techniques. In spite of its simplicity, k-NN classification is very attractive to practitioners, as it has shown very good performances in practical applications. However, it suffers from various drawbacks, like sensitivity to “noisy” prototypes and poor generalization properties when dealing with sparse,...
Most of the traditional classification methods behave undesirable, particularly producing poor predictive accuracy for the minority class of the imbalanced data from real world applications. This paper proposes a novel over-sampling strategy to handle imbalanced data based on cluster ensembles, named CE-SMOTE, which aims to provide a better training platform by introducing clustering consistency index...
The present work illustrates a Data Mining application in the meteorological domain. In particular, this work illustrates the creation of some fog classifying local indices, based on the post-processing of meteorological variables. A dataset containing a total amount of 17396 records, collected in Trapani Milo station and a poor quote of 142 fog events was obtained, such rare event required some specific...
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