Online Ensemble Learning of Data Streams with Gradually Evolved

Class evolution is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely class-based ensemble for class evolution.Emprical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. With the rapid development of incremental learning and online,learning,mining tasks in the context of data stream have been widely studied.Generally,data stream mining refers to the mining task that are conducted on a sequence of rapidly arriving data records. As the environment where the data are collected may change dynamically, the data distribution may also change accordingly. This phenomenon referred to as concept drift is one of the most important challenges in data stream mining. A data stream mining technique should be capable of constructing and dynamically updating a model in order to learn dynamic changes.