Partial example acquisition in cost-sensitive learning

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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. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning ? induction. General Terms Algorithms, Measurement, Performance, Economics. Keywords Data acquisition, induction, cost-sensitive learning, data mining, machine learning, active learning, active cost-se...
Victor S. Sheng, Charles X. Ling
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Victor S. Sheng, Charles X. Ling
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