Dynamically weighted majority voting for incremental learning and comparison of three boosting based approaches

We have previously introduced Learn++, an ensemble based incremental learning algorithm for acquiring new knowledge from data that later become available, even when such data introduce new classes. In this paper, we describe a modification to this algorithm, where the voting weights of the classifiers are updated dynamically based on the location of the test input in the feature space. The new algorithm provides improved performance, stronger immunity to catastrophic forgetting and finer balance to the stability-plasticity dilemma than its predecessor, particularly when new classes are introduced. The modified algorithm and its performance, as compared to Adaboost.Ml and the original Learn++, on real and benchmark datasets are presented.