Incremental learning of new classes in unbalanced datasets: Learn++.UDNC

Authors:
Gregory Ditzler;Michael D. Muhlbaier;Robi Polikar
Affiliations:
Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering, Rowan University, Glassboro, NJ;Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering, Rowan University, Glassboro, NJ;Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering, Rowan University, Glassboro, NJ
Venue:
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Year:
2010

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Abstract

We have previously described an incremental learning algorithm, Learn++.NC, for learning from new datasets that may include new concept classes without accessing previously seen data. We now propose an extension, Learn++.UDNC, that allows the algorithm to incrementally learn new concept classes from unbalanced datasets. We describe the algorithm in detail, and provide some experimental results on two separate representative scenarios (on synthetic as well as real world data) along with comparisons to other approaches for incremental and/or unbalanced dataset approaches.