Hybrid committee machine for incremental learning

In this paper we made four modifications to incremental ensemble learning algorithm Learn++, including (1) use a self-growing dynamic committee machine generated by error correlation partition (ECP) to construct individual hypothesis to avoid discard any hypothesis in learning; (2) in order to avoid the overall performance decline caused by dataset belonging to a single class. We use appropriate negative instances obtained by ECP to help to form classification boundaries; (3) adopt a new voting weights scheme with penalty term to allow the voting weights to vary in response to the confidence with which an instance is classified; (4) use a discrepancy measure to ensure differences between individual hypotheses to make it generalize better. Experiments show that this new hybrid committee machine for incremental learning Learn++.H sees further performance increase