document: Partial example acquisition in cost-sensitive learning It is often expensive to acquire data in real-world data mining applications . Most previous data mining and machine learning research , however , assumes that a fixed set of training examples is given . In this paper , we propose an online cost-sensitive framework that allows a learner to dynamically acquire examples as it learns , and to decide the ideal number of examples needed to minimize the total cost . We also propose a new strategy for Partial Example Acquisition ( PAS ) , in which the learner can acquire examples with a subset of attribute values to reduce the data acquisition cost . Experiments on UCI datasets show that the new PAS strategy is an effective method in reducing the total cost for data acquisition .
doc_bio_tags: O O O O O O O O O O O O O O O B I O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O