Repeated labeling using multiple noisy labelers

Panagiotis G. Ipeirotis, Foster Provost, Victor S. Sheng, Jing Wang

Research output: Contribution to journalArticle

78 Scopus citations

Abstract

This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction of predictive models. With the outsourcing of small tasks becoming easier, for example via Amazon's Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a set of robust techniques that combine different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.

Original languageEnglish
Pages (from-to)402-441
Number of pages40
JournalData Mining and Knowledge Discovery
Volume28
Issue number2
DOIs
StatePublished - Mar 2014

Keywords

  • Active learning
  • Classification
  • Data preprocessing
  • Data selection
  • Human computation
  • Repeated labeling
  • Selective labeling

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