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.
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Ditzler, G., Muhlbaier, M.D., Polikar, R. (2010). Incremental Learning of New Classes in Unbalanced Datasets: Learn + + .UDNC. In: El Gayar, N., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2010. Lecture Notes in Computer Science, vol 5997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12127-2_4
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DOI: https://doi.org/10.1007/978-3-642-12127-2_4
Publisher Name: Springer, Berlin, Heidelberg
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