Article ID Journal Published Year Pages File Type
403781 Neural Networks 2016 16 Pages PDF
Abstract

In this paper, a meta-cognitive online sequential extreme learning machine (MOS-ELM) is proposed for class imbalance and concept drift learning. In MOS-ELM, meta-cognition is used to self-regulate the learning by selecting suitable learning strategies for class imbalance and concept drift problems. MOS-ELM is the first sequential learning method to alleviate the imbalance problem for both binary class and multi-class data streams with concept drift. In MOS-ELM, a new adaptive window approach is proposed for concept drift learning. A single output update equation is also proposed which unifies various application specific OS-ELM methods. The performance of MOS-ELM is evaluated under different conditions and compared with methods each specific to some of the conditions. On most of the datasets in comparison, MOS-ELM outperforms the competing methods.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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