TY - GEN
T1 - Imbalanced Multiple Noisy Labeling for supervised learning
AU - Zhang, Jing
AU - Wu, Xindong
AU - Sheng, Victor S.
PY - 2013
Y1 - 2013
N2 - When labeling objects via Internet-based outsourcing systems, the labelers may have bias, because they lack expertise, dedication and personal preference. These reasons cause Imbalanced Multiple Noisy Labeling. To deal with the imbalance labeling issue, we propose an agnostic algorithm PLAT (Positive LAbel frequency Threshold) which does not need any information about quality of labelers and underlying class distribution. Simulations on eight realworld datasets with different underlying class distributions demonstrate that PLAT not only effectively deals with the imbalanced multiple noisy labeling problem that off-theshelf agnostic methods cannot cope with, but also performs nearly the same as majority voting under the circumstances that labelers have no bias.
AB - When labeling objects via Internet-based outsourcing systems, the labelers may have bias, because they lack expertise, dedication and personal preference. These reasons cause Imbalanced Multiple Noisy Labeling. To deal with the imbalance labeling issue, we propose an agnostic algorithm PLAT (Positive LAbel frequency Threshold) which does not need any information about quality of labelers and underlying class distribution. Simulations on eight realworld datasets with different underlying class distributions demonstrate that PLAT not only effectively deals with the imbalanced multiple noisy labeling problem that off-theshelf agnostic methods cannot cope with, but also performs nearly the same as majority voting under the circumstances that labelers have no bias.
UR - http://www.scopus.com/inward/record.url?scp=84893361669&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84893361669
SN - 9781577356158
T3 - Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
SP - 1651
EP - 1652
BT - Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
T2 - 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Y2 - 14 July 2013 through 18 July 2013
ER -