430 research outputs found
Get Another Label? Improving Data Quality and Data Mining
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 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.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
Get Another Label? Improving Data Quality and Data Mining
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 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.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
Repeated Labeling Using Multiple Noisy Labelers
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 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.This work was supported by the National Science Foundation under Grant
No. IIS-0643846, by an NSERC Postdoctoral Fellowship, and by an NEC
Faculty Fellowship
Learning to Predict the Wisdom of Crowds
The problem of "approximating the crowd" is that of estimating the crowd's
majority opinion by querying only a subset of it. Algorithms that approximate
the crowd can intelligently stretch a limited budget for a crowdsourcing task.
We present an algorithm, "CrowdSense," that works in an online fashion to
dynamically sample subsets of labelers based on an exploration/exploitation
criterion. The algorithm produces a weighted combination of a subset of the
labelers' votes that approximates the crowd's opinion.Comment: Presented at Collective Intelligence conference, 2012
(arXiv:1204.2991
Learning From Noisy Singly-labeled Data
Supervised learning depends on annotated examples, which are taken to be the
\emph{ground truth}. But these labels often come from noisy crowdsourcing
platforms, like Amazon Mechanical Turk. Practitioners typically collect
multiple labels per example and aggregate the results to mitigate noise (the
classic crowdsourcing problem). Given a fixed annotation budget and unlimited
unlabeled data, redundant annotation comes at the expense of fewer labeled
examples. This raises two fundamental questions: (1) How can we best learn from
noisy workers? (2) How should we allocate our labeling budget to maximize the
performance of a classifier? We propose a new algorithm for jointly modeling
labels and worker quality from noisy crowd-sourced data. The alternating
minimization proceeds in rounds, estimating worker quality from disagreement
with the current model and then updating the model by optimizing a loss
function that accounts for the current estimate of worker quality. Unlike
previous approaches, even with only one annotation per example, our algorithm
can estimate worker quality. We establish a generalization error bound for
models learned with our algorithm and establish theoretically that it's better
to label many examples once (vs less multiply) when worker quality is above a
threshold. Experiments conducted on both ImageNet (with simulated noisy
workers) and MS-COCO (using the real crowdsourced labels) confirm our
algorithm's benefits.Comment: 18 pages, 3 figure
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