مجموعه تلفیقی گسسته برای معادن، جریانهای داده ای را با ریزش مفهوم نامناسب مقایسه می کند

To overcome these issues, we propose an ensemble classifier called Gradual Resampling Ensemble (GRE). GRE could handle data streams which exhibit concept drifts and class imbalance. On the one hand, a selectively resampling method, where drifting data can be avoidable, is applied to select a part of previous minority examples for amplifying the current minority set. The disjuncts can be discovered by the DBSCAN clustering, and thus the influences of small disjuncts and outliers on the similarity evaluation can be avoidable. Only those minority examples with low probability of overlapping with the current majority set can be selected for resampling the current minority set. On the other hand, previous component classifiers are updated using latest instances. Thus, the ensemble could quickly adapt to a new condition, regardless types of concept drifts. Through the gradual oversampling of previous chunks using the current minority events, the class distribution of past chunks can be balanced. Favorable results in comparison to other algorithms suggest that GRE can maintain good performance on minority class, without sacrificing majority class performance.