EILearn: Learning Incrementally Using Previous Knowledge Obtained From an Ensemble of Classifiers

He majorchallenge in incremental learning is to learn from new data without accessing the previously observed data.This property fosters the stability-plasticity dilemma, where stability describes retaining the previously acquiredknowledge, and plasticity describes learning knowledge from new data. So, an ideal approach for incremental learning must find a balance between stability and plasticity. Focussing only on plasticity may lead to a situation, called catastrophic forgetting. At the same time, concentrating onlyon stability may lead to loss of knowledge. Only the relevant previous knowledge must be preserved. Irrelevant previous knowledge must be discarded, but with the ability that it can be recalled whenever required. Another challenge in incremental learning is learning in the presence of concept drift.