document: Active learning with direct query construction Active learning may hold the key for solving the data scarcity problem in supervised learning , i.e. , the lack of labeled data . Indeed , labeling data is a costly process , yet an active learner may request labels of only selected instances , thus reducing labeling work dramatically . Most previous works of active learning are , however , pool-based ; that is , a pool of unlabeled examples is given and the learner can only select examples from the pool to query for their labels . This type of active learning has several weaknesses . In this paper we propose novel active learning algorithms that construct examples directly to query for labels . We study both a specific active learner based on the decision tree algorithm , and a general active learner that can work with any base learning algorithm . As there is no restriction on what examples to be queried , our methods are shown to often query fewer examples to reduce the predictive error quickly . This casts doubt on the usefulness of the pool in pool-based active learning . Nevertheless , our methods can be easily adapted to work with a given pool of unlabeled examples .
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