Classifier evaluation under limited resources

Reuven Arbel, Lior Rokach

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Existing evaluations measures are insufficient when probabilistic classifiers are used for choosing objects to be included in a limited quota. This paper reviews performance measures that suit probabilistic classification and introduce two novel performance measures that can be used effectively for this task. It then investigates when to use each of the measures and what purpose each one of them serves. The use of these measures is demonstrated on a real life dataset obtained from the human resource field and is validated on set of benchmark datasets.

Original languageEnglish
Pages (from-to)1619-1631
Number of pages13
JournalPattern Recognition Letters
Volume27
Issue number14
DOIs
StatePublished - 15 Oct 2006

Keywords

  • Classification
  • Evaluation measures
  • Hit-rate
  • Recall
  • Receiver operating characteristic

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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