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Cited by 1,165

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Get another label? improving data quality and data mining using multiple, noisy labelers

Open access
Sheng, Victor S., Provost, Foster, Ipeirotis, Panagiotis G.·2008·Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
DOI 10.1145/1401890.1401965
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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. With the outsourcing of small tasks becoming easier, for example via Rent-A-Coder or 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 robust technique that combines 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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Metadata

Authors
Sheng, V. S., Provost, F., & Ipeirotis, P. G.
Venue
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher
ACM
Published
2008-08-24
Pages
614-622
DOI
10.1145/1401890.1401965
OpenAlex
W2125943921
Cited by
1,165
References
4
FWCI
34.49
Type
conference paper
Language
en
Open access
green

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