|As technology progresses with more and more data collected, the need of find-ing the appropriate label for them increases. However, many times the labeling process is a very difficult or/and expensive task and in most cases a help of an expert or expensive equipment is needed. For this reason the need of labeling only the most appropriate instances rises. Active Learning techniques can ac-complish this by querying only those instances that a trained model finds the greatest amount of information and providing them to a human expert in order to label them. Combining these techniques with a fast ensemble classifier, a very performant in terms of classification accuracy schema can emerge where a trained model in a small amount of labeled instances can grow by adding only the most informative instances from a much greater pool of unlabeled instances. In this paper, we will propose such a schema using Bagging Ensemble Selec-tion that uses REPTree as base classifier under Active Learning techniques and we will compare it to four well-known ensemble classifiers under the same techniques on 61 real world datasets.|
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