TY - GEN
T1 - Evaluation of sampling methods for learning from imbalanced data
AU - Goel, Garima
AU - Maguire, Liam
AU - Li, Yuhua
AU - McLoone, Sean
PY - 2013/8/28
Y1 - 2013/8/28
N2 - The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics.
AB - The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics.
KW - imbalanced data
KW - sampling methods
KW - support vector machines
UR - http://www.scopus.com/inward/record.url?scp=84882745695&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39479-9_47
DO - 10.1007/978-3-642-39479-9_47
M3 - Conference contribution
AN - SCOPUS:84882745695
SN - 9783642394782
VL - 7995 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 392
EP - 401
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 9th International Conference on Intelligent Computing, ICIC 2013
Y2 - 28 July 2013 through 31 July 2013
ER -