IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v57y2013i1p377-391.html
   My bibliography  Save this article

Drift mining in data: A framework for addressing drift in classification

Author

Listed:
  • Hofer, Vera
  • Krempl, Georg

Abstract

A novel statistical methodology for analysing population drift in classification is introduced. Drift denotes changes in the joint distribution of explanatory variables and class labels over time. It entails the deterioration of a classifier’s performance and requires the optimal decision boundary to be adapted after some time. However, in the presence of verification latency a re-estimation of the classification model is impossible, since in such a situation only recent unlabelled data are available, and the true corresponding labels only become known after some lapse in time. For this reason a novel drift mining methodology is presented which aims at detecting changes over time. It allows us either to understand evolution in the data from an ex-post perspective or, ex-ante, to anticipate changes in the joint distribution. The proposed drift mining technique assumes that the class priors change by a certain factor from one time point to the next, and that the conditional distributions do not change within this time period. Thus, the conditional distributions can be estimated at a time where recent labelled data are available. In subsequent periods the unconditional distribution can be expressed as a mixture of the conditional distributions, where the mixing proportions are equal to the class priors. However, as the unconditional distributions can also be estimated from new unlabelled data, they can then be compared to the mixture representation by means of least-squares criteria. This allows for easy and fast estimation of the changes in class prior values in the presence of verification latency. The usefulness of this drift mining approach is demonstrated using a real-world dataset from the area of credit scoring.

Suggested Citation

  • Hofer, Vera & Krempl, Georg, 2013. "Drift mining in data: A framework for addressing drift in classification," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 377-391.
  • Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:377-391
    DOI: 10.1016/j.csda.2012.07.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312002812
    Download Restriction: Full text for ScienceDirect subscribers only.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. I. D. Currie & M. Durban & P. H. C. Eilers, 2006. "Generalized linear array models with applications to multidimensional smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 259-280.
    2. Hand D.J. & Vinciotti V., 2003. "Local Versus Global Models for Classification Problems: Fitting Models Where it Matters," The American Statistician, American Statistical Association, vol. 57, pages 124-131, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hofer, Vera, 2015. "Adapting a classification rule to local and global shift when only unlabelled data are available," European Journal of Operational Research, Elsevier, vol. 243(1), pages 177-189.
    2. Dirk Tasche, 2014. "Exact fit of simple finite mixture models," Papers 1406.6038, arXiv.org, revised Jul 2014.
    3. Dirk Tasche, 2014. "Exact Fit of Simple Finite Mixture Models," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 7(4), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:377-391. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/csda .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.