Get Another Labels? Improving Data Quality and Data Mining Using Multiple Noisy Labelers
This talk presents 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.
Victor S. Sheng received the master’s degree in computer science from the University of New Brunswick, Fredericton, NB, Canada, in 2003, and the Ph.D. degree in computer science from the Western University, London, ON, Canada, in 2007. He was an Associate Research Scientist and NSERC Post-Doctoral Fellow in information systems at Stern Business School, New York University, New York, NY, USA.
He is an Associate Professor of Computer Science at the University of Central Arkansas, Conway, AR, USA, and the Founding Director of Data Analytics Laboratory. His current research interests include data mining, machine learning, and related applications. Prof. Sheng is a senior member of the IEEE and the IEEE Computer Society, and a Lifetime Member of ACM. He has published more than 80 research papers in conferences and journals of machine learning and data mining. He was the recipient of the Best Paper Award Runner-Up from KDD’08, and the Best Paper Award from ICDM’11. He is a PC Member for a number of international conferences and a reviewer for several international journals.