Contextual One-Class Classification in Data Streams
This work addresses the gap in applying contextual learning to one-class classification in dynamic data streams, which is incremental as it extends static data methods to streaming environments.
The paper tackled the problem of one-class classification in data streams by proposing the use of contexts to improve classifier performance, concluding that this approach can enhance streaming one-class classifiers.
In machine learning, the one-class classification problem occurs when training instances are only available from one class. It has been observed that making use of this class's structure, or its different contexts, may improve one-class classifier performance. Although this observation has been demonstrated for static data, a rigorous application of the idea within the data stream environment is lacking. To address this gap, we propose the use of context to guide one-class classifier learning in data streams, paying particular attention to the challenges presented by the dynamic learning environment. We present three frameworks that learn contexts and conduct experiments with synthetic and benchmark data streams. We conclude that the paradigm of contexts in data streams can be used to improve the performance of streaming one-class classifiers.