Sequential extreme learning machines for class imbalance and concept drift
Date of Issue2015
School of Electrical and Electronic Engineering
Class imbalance and concept drift are two problems commonly exist in sequential learning. A weighted online sequential extreme learning machine (WOS-ELM) algorithm is proposed that has a distinctive feature of class imbalance learning (CIL) in both the chunk-by-chunk and one-by-one modes. A new sample can update the classifier without waiting for a chunk to be completed. For CIL in drifting environments, a computationally efficient framework, referred to as ensemble of subset online sequential extreme learning machine is proposed. It comprises a main ensemble representing short-term memory, an information storage module representing long-term memory and a change detector to promptly detect concept drifts. A self-regulatory method, referred to as meta-cognitive online sequential extreme learning machine, is proposed to adapt the learning according to the nature of data stream i.e. select appropriate strategy for class imbalance and concept drift learning. A single OS-ELM equation is proposed for multiclass imbalance and concept drift learning.
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems