Get another label? improving data quality and data mining 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 …
imperfect. We examine the improvement (or lack thereof) in data quality via repeated …
Active learning: A survey
In all these cases, labels can be obtained, but only at a significant cost to the end user. An
important observation is that all records are not equally important from the perspective of …
important observation is that all records are not equally important from the perspective of …
Efficiently learning the accuracy of labeling sources for selective sampling
P Donmez, JG Carbonell, J Schneider - Proceedings of the 15th ACM …, 2009 - dl.acm.org
Many scalable data mining tasks rely on active learning to provide the most useful
accurately labeled instances. However, what if there are multiple labeling sources ('oracles' …
accurately labeled instances. However, what if there are multiple labeling sources ('oracles' …
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 …
imperfect. We examine the improvement (or lack thereof) in data quality via repeated …
Adaptive sampling strategies to construct equitable training datasets
W Cai, R Encarnacion, B Chern… - Proceedings of the …, 2022 - dl.acm.org
In domains ranging from computer vision to natural language processing, machine learning
models have been shown to exhibit stark disparities, often performing worse for members of …
models have been shown to exhibit stark disparities, often performing worse for members of …
Active feature-value acquisition
M Saar-Tsechansky, P Melville… - Management …, 2009 - pubsonline.informs.org
Most induction algorithms for building predictive models take as input training data in the
form of feature vectors. Acquiring the values of features may be costly, and simply acquiring …
form of feature vectors. Acquiring the values of features may be costly, and simply acquiring …
Icebreaker: Element-wise efficient information acquisition with a bayesian deep latent gaussian model
In this paper, we address the ice-start problem, ie, the challenge of deploying machine
learning models when only a little or no training data is initially available, and acquiring …
learning models when only a little or no training data is initially available, and acquiring …
Class imbalance and active learning
J Attenberg, Ş Ertekin - Imbalanced Learning: Foundations …, 2013 - Wiley Online Library
This chapter focuses on the interaction between active learning (AL) and class imbalance,
discussing (i) AL techniques designed specifically for dealing with imbalanced settings,(ii) …
discussing (i) AL techniques designed specifically for dealing with imbalanced settings,(ii) …
Active feature acquisition with supervised matrix completion
Feature missing is a serious problem in many applications, which may lead to low quality of
training data and further significantly degrade the learning performance. While feature …
training data and further significantly degrade the learning performance. While feature …
Learning to limit data collection via scaling laws: A computational interpretation for the legal principle of data minimization
Modern machine learning systems are increasingly characterized by extensive personal
data collection, despite the diminishing returns and increasing societal costs of such …
data collection, despite the diminishing returns and increasing societal costs of such …