Thursday, May 20, 2021 - 4:00pm to 5:00pm
Location:Virtual Presentation - ET Remote Access - Zoom
Speaker:ELLANGO JOTHIMURUGESAN, Ph.D. Student https://www.pdl.cmu.edu/People/ellango.shtml
DriftSurf: Stable-State / Reactive-State Learning under Concept Drift
When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model. We present an adaptive learning algorithm that extends previous drift-detection-based methods by incorporating drift detection into a broader stable-state/reactive-state process. The advantage of our approach is that we can use aggressive drift detection in the stable state to achieve a high detection rate, but mitigate the false positive rate of standalone drift detection via a reactive state that reacts quickly to true drifts while eliminating most false positives. Our theoretical analysis shows that the risk of the algorithm is competitive to an algorithm with oracle knowledge of when (abrupt) drifts occur. Experiments on synthetic and real datasets with concept drifts confirm our theoretical analysis.
Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.
Zoom Participation. See announcement.