1 research outputs found
scikit-dyn2sel -- A Dynamic Selection Framework for Data Streams
Mining data streams is a challenge per se. It must be ready to deal with an
enormous amount of data and with problems not present in batch machine
learning, such as concept drift. Therefore, applying a batch-designed
technique, such as dynamic selection of classifiers (DCS) also presents a
challenge. The dynamic characteristic of ensembles that deal with streams
presents barriers to the application of traditional DCS techniques in such
classifiers. scikit-dyn2sel is an open-source python library tailored for
dynamic selection techniques in streaming data. scikit-dyn2sel's development
follows code quality and testing standards, including PEP8 compliance and
automated high test coverage using codecov.io and circleci.com. Source code,
documentation, and examples are made available on GitHub at
https://github.com/luccaportes/Scikit-DYN2SEL.Comment: Paper introducing scikit-dyn2sel, a dynamic selection framework for
data stream