Partial example acquisition in cost-sensitive learning

Victor S. Sheng, Charles X. Ling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish
Title of host publicationKDD-2007
Subtitle of host publicationProceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages638-646
Number of pages9
DOIs
StatePublished - 2007
EventKDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - San Jose, CA, United States
Duration: Aug 12 2007Aug 15 2007

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

ConferenceKDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CitySan Jose, CA
Period08/12/0708/15/07

Keywords

  • Active cost-sensitive learning
  • Active learning
  • Cost-sensitive learning
  • Data acquisition
  • Data mining
  • Induction
  • Interactive and online data mining
  • Machine learning

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  • Cite this

    Sheng, V. S., & Ling, C. X. (2007). Partial example acquisition in cost-sensitive learning. In KDD-2007: Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 638-646). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1281192.1281261