Reviews: Learning with Feature Evolvable Streams

This paper formalizes a new problem setting, Feature Evolvable Streaming Learning. Sensors or other devices to extract feature values have the limited lifespans; therefore, these devices have been periodically replaced and the associated feature space changes. This learning paradigm prepares the overlapping period to adapt to the new feature space. In this overlapping period, learning algorithms receive features from both the old devices and the new devices simultaneously to capture the relationship between two feature spaces. This paper develops two learning algorithms to efficiently use previous experiences extracted from old training data to train/predict in the new feature space: 1) the weighted combination based predictor ensemble method, 2) the dynamic classifier selection.