TY - GEN
T1 - Empowering imbalanced data in supervised learning
T2 - 24th International Conference on Artificial Neural Networks, ICANN 2014
AU - Almogahed, Bassam A.
AU - Kakadiaris, Ioannis A.
PY - 2014
Y1 - 2014
N2 - We present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a well-defined training set. We present extensive experimental results, which demonstrate that the proposed framework significantly outperforms all other sampling algorithms in 67% of the cases across three different classifiers and ranks second best for the remaining 33% of the cases.
AB - We present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a well-defined training set. We present extensive experimental results, which demonstrate that the proposed framework significantly outperforms all other sampling algorithms in 67% of the cases across three different classifiers and ranks second best for the remaining 33% of the cases.
UR - http://www.scopus.com/inward/record.url?scp=84958550816&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958550816&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11179-7_66
DO - 10.1007/978-3-319-11179-7_66
M3 - Conference contribution
AN - SCOPUS:84958550816
SN - 9783319111780
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 523
EP - 530
BT - Artificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
PB - Springer-Verlag
Y2 - 15 September 2014 through 19 September 2014
ER -