@inproceedings{107e6b5dd29840a8af9472ef8a096b1c,
title = "Partial example acquisition in cost-sensitive learning",
abstract = "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.",
keywords = "Active cost-sensitive learning, Active learning, Cost-sensitive learning, Data acquisition, Data mining, Induction, Interactive and online data mining, Machine learning",
author = "Sheng, {Victor S.} and Ling, {Charles X.}",
year = "2007",
doi = "10.1145/1281192.1281261",
language = "English",
isbn = "1595936092",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
pages = "638--646",
booktitle = "KDD-2007",
note = "null ; Conference date: 12-08-2007 Through 15-08-2007",
}