TY - GEN
T1 - An incremental learning algorithm for non-stationary environments and class imbalance
AU - Ditzler, Gregory
AU - Polikar, Robi
AU - Chawla, Nitesh
PY - 2010
Y1 - 2010
N2 - Learning in a non-stationary environment and in the presence of class imbalance has been receiving more recognition from the computational intelligence community, but little work has been done to create an algorithm or a framework that can handle both issues simultaneously. We have recently introduced a new member to the Learn++ family of algorithms, Learn++.NSE, which is designed to track non-stationary environments. However, this algorithm does not work well when there is class imbalance as it has not been designed to handle this problem. On the other hand, SMOTE - a popular algorithm that can handle class imbalance - is not designed to learn in nonstationary environments because it is a method of oversampling the data. In this work we describe and present preliminary results for integrating SMOTE and Learn++.NSE to create an algorithm that is robust to learning in a nonstationary environment and under class imbalance.
AB - Learning in a non-stationary environment and in the presence of class imbalance has been receiving more recognition from the computational intelligence community, but little work has been done to create an algorithm or a framework that can handle both issues simultaneously. We have recently introduced a new member to the Learn++ family of algorithms, Learn++.NSE, which is designed to track non-stationary environments. However, this algorithm does not work well when there is class imbalance as it has not been designed to handle this problem. On the other hand, SMOTE - a popular algorithm that can handle class imbalance - is not designed to learn in nonstationary environments because it is a method of oversampling the data. In this work we describe and present preliminary results for integrating SMOTE and Learn++.NSE to create an algorithm that is robust to learning in a nonstationary environment and under class imbalance.
KW - Class imbalance
KW - Multiple classifier systems
KW - Nonstationary environments
UR - https://www.scopus.com/pages/publications/78149483725
UR - https://www.scopus.com/pages/publications/78149483725#tab=citedBy
U2 - 10.1109/ICPR.2010.734
DO - 10.1109/ICPR.2010.734
M3 - Conference contribution
AN - SCOPUS:78149483725
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2997
EP - 3000
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -