On the Soundness and Security of Privacy-Preserving SVM for Outsourcing Data Classification

Online Ensemble Learning of Data Streams with Gradually Evolved Classes

Abstract

Class evolution, the phenomenon of class emergence and disappearance, 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 classesemerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble forClass Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods.

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