Online entropy-based discretization for data streaming classification

In order to address all the aforementioned problems, we propose a novel, online and self-adaptive discretization solution for streaming classification which aims at reducing the negative impact of fluctuations in evolving intervals. Experiments with a long list of standard streaming datasets and discretizers have demonstrated that our proposal performs significantly more accurately than the other alternatives. In addition, our scheme is able to leverage from class information without incurring in an overweight cost, being ranked as one of the most rapid supervised options.