Journal
DATA MINING AND KNOWLEDGE DISCOVERY
Volume 28, Issue 2, Pages 402-441Publisher
SPRINGER
DOI: 10.1007/s10618-013-0306-1
Keywords
Active learning; Data selection; Data preprocessing; Classification; Human computation; Repeated labeling; Selective labeling
Funding
- National Science Foundation [IIS-0643846, IIS-1115417]
- NSERC Postdoctoral Fellowship
- NEC Faculty Fellowship
- Google Focused Award
- George Kellner Fellowship
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1115417] Funding Source: National Science Foundation
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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.
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