We consider binary classification problems where each of the two classes show multi-modal distribution in the feature space. Inspired by existing ensemble learning methods for multi-class classification, we develop ensemble learning methods for binary classification that make use of the bipartite nature of the positive and negative modes in the data. By constructing ensembles that make use of the multi-modal structure within the two classes, as opposed to using random samples, we are able to ensure sufficient diversity among the classifiers and adequate representation of the modes in the learning of the classifiers. We demonstrate the effectiveness of the proposed ensemble learning methods in comparison with existing approaches over a synthetic dataset and a real-world application involving global lake monitoring, over a broad range of base classifiers.
|Original language||English (US)|
|Title of host publication||SIAM International Conference on Data Mining 2015, SDM 2015|
|Editors||Jieping Ye, Suresh Venkatasubramanian|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2015|
|Event||SIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada|
Duration: Apr 30 2015 → May 2 2015
|Name||SIAM International Conference on Data Mining 2015, SDM 2015|
|Other||SIAM International Conference on Data Mining 2015, SDM 2015|
|Period||4/30/15 → 5/2/15|
Bibliographical noteFunding Information:
Acknowledgments: This research was supported in part by the National Science Foundation Awards 1029711 and 0905581, and the NASA Award NNX12AP37G. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.
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