Evaluating Extreme Learning Machine Models in the Presence of Concept Drift in Streaming Data
Tagrid Abdullah N. Alshalali, Darsana P. Josyula · 2020
This paper discusses concept drift in online streaming data and evaluates the performance of different Extreme Learning Machine (ELM) based techniques on classifying online streaming data in the presence of concept drift. It also compares the performance of a hybrid model called Online Recurrent ELM (OR-ELM) with traditional recurrent neural networks, in terms of training speed and accuracy, on streaming data that has concept drift. The results of our experiments show that OR-ELM has better accuracy and faster training time.