Feature selection plays an important role in Machine Learning pipelines, and many challenges emerge for feature selection when data arrives continuously as a stream.In this paper, we extend the Adaptive Boosting for Feature Selection (ABFS) algorithm by (i) using a different Online Boosting strategy and (ii) changing the Boosting scaling factor of instances weighting.Results show that our extended ABFS leveraged the predictive performance of classifiers more than the standard ABFS in the most used monolithic classifiers for stream mining.2 Feature Selection with Boosting Decision Stumps 1 https://sites.google.com

Dynamic classifier selection (DCS) regards well-known machine learning techniques in the batch setting that leverage ensemble performance. Most of the methods use similarity-based methods as a proxy, culminating in high computation costs and becoming unfeasible in many streaming scenarios. In this paper, we propose a DCS method able to cope with the high-speed streaming setting, which is based on the performance of base learners in the most recent instances. The impact of our method is evaluated with different ensembles for data streams. We also propose modifications to an Online Boosting method, which has its performance improved with DCS. Our method increases the accuracy and kappa statistic of state-of-the-art ensembles with low overhead of time processing and memory.