Noise detection in semi-supervised learning with the use of data streams

Noise detection in semi-supervised learning with the use of data streams

Blog Article

Often, it is necessary to construct training sets.If we have only a small number of tagged objects and a large group of unlabeled objects, we can build the training set simulating a data stream of unlabelled objects from which it is necessary to learn and to incorporate them to the training set later.In order to PTO and Pump Products prevent deterioration of the training set obtained, in this work we propose a scheme that takes into account the concept drift, since in many situations the distribution of classes may change over time.To classify the unlabelled objects we have used an ensemble wellness of classifiers and we propose a strategy to detect the noise after the classification process.