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Active Feature-Value Acquisition

Author

Listed:
  • Maytal Saar-Tsechansky

    (McCombs School of Business, University of Texas at Austin, Austin, Texas 78712)

  • Prem Melville

    (IBM T.J. Watson Research Center, Yorktown Heights, New York 10598)

  • Foster Provost

    (Stern School of Business, New York University, New York, New York 10012)

Abstract

Most induction algorithms for building predictive models take as input training data in the form of feature vectors. Acquiring the values of features may be costly, and simply acquiring all values may be wasteful or prohibitively expensive. Active feature-value acquisition (AFA) selects features incrementally in an attempt to improve the predictive model most cost-effectively. This paper presents a framework for AFA based on estimating information value. Although straightforward in principle, estimations and approximations must be made to apply the framework in practice. We present an acquisition policy, sampled expected utility (SEU), that employs particular estimations to enable effective ranking of potential acquisitions in settings where relatively little information is available about the underlying domain. We then present experimental results showing that, compared with the policy of using representative sampling for feature acquisition, SEU reduces the cost of producing a model of a desired accuracy and exhibits consistent performance across domains. We also extend the framework to a more general modeling setting in which feature values as well as class labels are missing and are costly to acquire.

Suggested Citation

  • Maytal Saar-Tsechansky & Prem Melville & Foster Provost, 2009. "Active Feature-Value Acquisition," Management Science, INFORMS, vol. 55(4), pages 664-684, April.
  • Handle: RePEc:inm:ormnsc:v:55:y:2009:i:4:p:664-684
    DOI: 10.1287/mnsc.1080.0952
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    References listed on IDEAS

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    1. Kevin F. McCardle, 1985. "Information Acquisition and the Adoption of New Technology," Management Science, INFORMS, vol. 31(11), pages 1372-1389, November.
    2. Zhiqiang Zheng & Balaji Padmanabhan, 2006. "Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution," Management Science, INFORMS, vol. 52(5), pages 697-712, May.
    3. Michael V. Mannino & Vijay S. Mookerjee, 1999. "Optimizing Expert Systems: Heuristics for Efficiently Generating Low-Cost Information Acquisition Strategies," INFORMS Journal on Computing, INFORMS, vol. 11(3), pages 278-291, August.
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    Citations

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    Cited by:

    1. Meghana Deodhar & Joydeep Ghosh & Maytal Saar-Tsechansky & Vineet Keshari, 2017. "Active Learning with Multiple Localized Regression Models," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 503-522, August.
    2. Zhepeng Li & Xiao Fang & Xue Bai & Olivia R. Liu Sheng, 2017. "Utility-Based Link Recommendation for Online Social Networks," Management Science, INFORMS, vol. 63(6), pages 1938-1952, June.
    3. Shantanu Gupta & Zachary C. Lipton & David Childers, 2021. "Efficient Online Estimation of Causal Effects by Deciding What to Observe," Papers 2108.09265, arXiv.org, revised Oct 2021.
    4. Kaiquan Xu & Stephen Shaoyi Liao & Raymond Y. K. Lau & J. Leon Zhao, 2014. "Effective Active Learning Strategies for the Use of Large-Margin Classifiers in Semantic Annotation: An Optimal Parameter Discovery Perspective," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 461-483, August.
    5. Xiaoping Liu & Xiao-Bai Li & Sumit Sarkar, 2023. "Cost-Restricted Feature Selection for Data Acquisition," Management Science, INFORMS, vol. 69(7), pages 3976-3992, July.
    6. Hung-Pin Kao & Kwei Tang, 2014. "Cost-Sensitive Decision Tree Induction with Label-Dependent Late Constraints," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 238-252, May.
    7. Jing Wang & Panagiotis G. Ipeirotis & Foster Provost, 2017. "Cost-Effective Quality Assurance in Crowd Labeling," Information Systems Research, INFORMS, vol. 28(1), pages 137-158, March.
    8. Fan Zhou & Kunpeng Zhang & Bangying Wu & Yi Yang & Harry Jiannan Wang, 2021. "Unifying Online and Offline Preference for Social Link Prediction," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1400-1418, October.

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