Learning Framework for Non-stationary and Imbalanced Data Stream

摘要

Although learning on non-stationary data and imbalanced data have been extensively studiedin the literature separately, however little work has been done to tackle the imbalanced issue on nonstationarydata stream as the joint probability distribution between the data and classes changes withtime and may results skewed class distribution. Especially in airlines delay detection, data sources aredynamic generated at high speed in real time, type of delay activity changes with time and in each chunkof stream, delay detection instances are less so concept drift and class imbalanced issues arisessimultaneously. Through this research work we propose an ensemble based incremental learningapproach towards non-stationary imbalanced data stream.