Online Streaming Feature Selection: a Review

Online streaming features (OSF) is an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with time as new features stream in. The critical challenge for online streaming feature selection (OSFS) is the unavailability of the entire feature set before learning starts. In this paper, we make the interested reader aware of the possibilities of OSFS, providing a basic taxonomy of OSFS techniques, and discussing their use, variety and potential in a number of both common as well as upcoming applications.

Online streaming features (OSF) is an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with time as new features stream in. The critical challenge for online streaming feature selection (OSFS) is the unavailability of the entire feature set before learning starts. In this paper, we make the interested reader aware of the possibilities of OSFS, providing a basic taxonomy of OSFS techniques, and discussing their use, variety and potential in a number of both common as well as upcoming applications.