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Background Check: A General Technique to Build More Reliable and Versatile Classifiers

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

Original languageEnglish
Title of host publication2016 IEEE 16th International Conference on Data Mining (ICDM 2016)
Subtitle of host publicationProceedings of a meeting held 12-15 December 2016, Barcelona, Spain
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781509054725
ISBN (Print)9781509054749
DateAccepted/In press - 26 Sep 2016
DateE-pub ahead of print - 2 Feb 2017
DatePublished (current) - Mar 2017
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: 12 Dec 201615 Dec 2016

Publication series

NameProceedings of the IEEE International Conference on Data Mining (ICDM)
ISSN (Print)2375-8486


Conference16th IEEE International Conference on Data Mining, ICDM 2016
CityBarcelona, Catalonia


We introduce a powerful technique to make classifiers more reliable and versatile. Background Check equips classifiers with the ability to assess the difference of unlabelled test data from the training data. In particular, Background Check gives classifiers the capability to (i) perform cautious classification with a reject option; (ii) identify outliers; and (iii) better assess the confidence in their predictions. We derive the method from first principles and consider four particular relationships between background and foreground distributions. One of these assumes an affine relationship with two parameters, and we show how this bivariate parameter space naturally interpolates between the above capabilities.We demonstrate the versatility of the approach by comparing it experimentally with published special-purpose solutions for outlier detection and confident classification on 41 benchmark datasets. Results show that Background Check can match and in many cases surpass the performances of specialised approaches.

    Research areas

  • Training data, Estimation, Standards, Reliability, Data models, Probability, Conferences, Multiclass classification, Outlier detection, Confident classification

    Structured keywords

  • Jean Golding


16th IEEE International Conference on Data Mining, ICDM 2016

Duration12 Dec 201615 Dec 2016
CityBarcelona, Catalonia
SponsorsIEEE Computer Society (External organisation)

Event: Conference

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 201 KB, PDF document


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