title: Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification.
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.
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. Electronic address: bilal2@e.ntu.edu.sg.
authors.forename: Bilal
authors.initials: B
authors.lastname: Mirza
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. Electronic address: ezplin@ntu.edu.sg.
authors.forename: Zhiping
authors.initials: Z
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D003071:Cognition; D020407:Internet; D000069550:Machine Learning; D012691:Serial Learning
keywords: Concept drift; Extreme learning machine; Meta-cognition; Multi-class imbalance; Sequential learning