Abstract:  Most of the existing detection methods for concept evolution are essentially based on supervised learning and are often used to solve the problem that only one novel class appears in a period of time. However, they can not handle the task of a class disappearing and recurring in streaming data. To address the above problems, an adaptive detection method for concept evolution based on weakly supervised ensemble (AD_WE) is proposed. The weakly supervised ensemble strategy is used to construct an ensemble learner to make local predictions on the training samples in the data block. Similar data with strong cohesion in the feature space are detected and clustered using local density and relative distance. The similarity of the clustering results is then compared to detect novel class instances and distinguish between different novel classes. And a dynamic decay model is established according to the characteristics of data change over time. The vanished class is eliminated in time, and the recurring class is detected through similarity comparison. Experiments show that the proposed method can respond to concept evolution in a timely manner, effectively identify vanished classes and recurring classes, and improve the generalization performance of the learner.

Key words: concept evolution, weakly supervised ensemble, adaptive model, dynamic decay model, vanished class, recurring class