2008 GetAnotherLabelImprovingDataQua

(Sheng et al., 2008) ⇒ Victor S. Sheng, Foster Provost, and Panagiotis G. Ipeirotis. (2008). “Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401965

Subject Headings: Iterative Labeling, Active Learning.

Notes

Cited By

2015

(Russakovsky et al., 2015) ⇒ Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang et al. (2015). “Imagenet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115, no. 3

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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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 GetAnotherLabelImprovingDataQua	Foster Provost Panagiotis G. Ipeirotis Victor S. Sheng			Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers		KDD-2008 Proceedings		10.1145/1401890.1401965		2008