概念飄移近年來已成為資料探勘領域中分析非平穩分佈的重要議題，此外，資料串流上也有傾斜的類別分布，被稱為類別不平衡。實際上，在現實世界中，資料串流可能同時具有多個概念漂移和類別不平衡分佈。然而，由於大多數的研究方法並沒有同時地考慮類別不平衡和概念飄移問題，它們可能在整體平均準確度上有不錯的表現，但在少數類別的準確度卻是非常差，為了應對這些挑戰，本篇論文提出了一種可以進一步在有概念飄移的不平衡資料串流上提高少數類別準確度的新加權方法。實驗結果證實了我們的方法不僅可以在平均準確度上達到不錯的表現，而且還提高了在不平衡的資料串流上少數類別的準確度。

Concept drift has become an important issue while analyzing data streams. Further, data streams can also have skewed class distributions, known as class imbalance. Actually, in the real world, it is likely that a data stream simultaneously has multiple concept drifts and an imbalanced class distribution. However, since most research approaches do not consider class imbalance and the concept drift problem at the same time, they probably have a good performance on the overall average accuracy, while the accuracy of the minority class is very poor. To deal with these challenges, this paper proposes a new weighting method which can further improve the accuracy of the minority class on the imbalanced data streams with concept drifts. The experimental results confirm that our method not only achieves an impressive performance on the average accuracy but also improves the accuracy of the minority class on the imbalanced data streams.

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