TY - GEN
T1 - Hellinger distance based drift detection for nonstationary environments
AU - Ditzler, Gregory
AU - Polikar, Robi
PY - 2011
Y1 - 2011
N2 - Most machine learning algorithms, including many online learners, assume that the data distribution to be learned is fixed. There are many real-world problems where the distribution of the data changes as a function of time. Changes in nonstationary data distributions can significantly reduce the generalization ability of the learning algorithm on new or field data, if the algorithm is not equipped to track such changes. When the stationary data distribution assumption does not hold, the learner must take appropriate actions to ensure that the new/relevant information is learned. On the other hand, data distributions do not necessarily change continuously, necessitating the ability to monitor the distribution and detect when a significant change in distribution has occurred. In this work, we propose and analyze a feature based drift detection method using the Hellinger distance to detect gradual or abrupt changes in the distribution.
AB - Most machine learning algorithms, including many online learners, assume that the data distribution to be learned is fixed. There are many real-world problems where the distribution of the data changes as a function of time. Changes in nonstationary data distributions can significantly reduce the generalization ability of the learning algorithm on new or field data, if the algorithm is not equipped to track such changes. When the stationary data distribution assumption does not hold, the learner must take appropriate actions to ensure that the new/relevant information is learned. On the other hand, data distributions do not necessarily change continuously, necessitating the ability to monitor the distribution and detect when a significant change in distribution has occurred. In this work, we propose and analyze a feature based drift detection method using the Hellinger distance to detect gradual or abrupt changes in the distribution.
KW - concept drift
KW - drift detection
KW - nonstationary environments
UR - http://www.scopus.com/inward/record.url?scp=79961240773&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79961240773&partnerID=8YFLogxK
U2 - 10.1109/CIDUE.2011.5948491
DO - 10.1109/CIDUE.2011.5948491
M3 - Conference contribution
AN - SCOPUS:79961240773
SN - 9781424499311
T3 - IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDUE 2011: 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments
SP - 41
EP - 48
BT - IEEE SSCI 2011
T2 - Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2011
Y2 - 11 April 2011 through 15 April 2011
ER -