TY - GEN
T1 - Positive and Unlabeled Learning Algorithms and Applications
T2 - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
AU - Jaskie, Kristen
AU - Spanias, Andreas
N1 - Funding Information:
This work is funded in part by the NSF I/UCRC #1540040 and the ASU SenSIP center.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This paper will address the Positive and Unlabeled learning problem (PU learning) and its importance in the growing field of semi-supervised learning. In most real-world classification applications, well labeled data is expensive or impossible to obtain. We can often label a small subset of data as belonging to the class of interest. It is frequently impractical to manually label all data we are not interested in. We are left with a small set of positive labeled items of interest and a large set of unknown and unlabeled data. Learning a model for this is the PU learning problem.In this paper, we explore several applications for PU learning including examples in biological/medical, business, security, and signal processing. We then survey the literature for new and existing solutions to the PU learning problem.
AB - This paper will address the Positive and Unlabeled learning problem (PU learning) and its importance in the growing field of semi-supervised learning. In most real-world classification applications, well labeled data is expensive or impossible to obtain. We can often label a small subset of data as belonging to the class of interest. It is frequently impractical to manually label all data we are not interested in. We are left with a small set of positive labeled items of interest and a large set of unknown and unlabeled data. Learning a model for this is the PU learning problem.In this paper, we explore several applications for PU learning including examples in biological/medical, business, security, and signal processing. We then survey the literature for new and existing solutions to the PU learning problem.
KW - Artificial intelligence
KW - Classification
KW - Machine learning
KW - PU learning
KW - Positive unlabeled learning
UR - http://www.scopus.com/inward/record.url?scp=85075858037&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075858037&partnerID=8YFLogxK
U2 - 10.1109/IISA.2019.8900698
DO - 10.1109/IISA.2019.8900698
M3 - Conference contribution
AN - SCOPUS:85075858037
T3 - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
BT - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 July 2019 through 17 July 2019
ER -