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This paper presents results of classification on imbalanced data with ensemble allocation method. The result of the allocation method were compared to traditional techniques for dealing with imbalaced datasets – the sampling methods. The allocation method is a two level ensemble that combines unsupervised and supervised learning. In this research the first level of allocation the unsupervised anomaly detection is used as an allocator which is combined with several traditional classification method on second level of ensemble. The allocation method is tested on imbalanced datasets and the results are compared to two well used sampling methods – under-sampling of majority instances, and over-sampling with SMOTE which introduces new artificial instances of minority class to the dataset. Results of all of the methods were compared on overall accuracy and average F-score metrics. The results show that allocation method produces the best classification model, which is also supported by statistical analysis.
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