Modified Deep ELM to Detect and Adapt Concept Drift in Data Stream

Extreme learning machine (ELM) is a feedforward neural network (SLFN) which works with a single hidden layer. ELM is popularly used in data stream classification due to its speed and accuracy. In this paper, a modified deep extreme learning machine (ELM) is presented. In this proposed Deep ELM classifier, a concept drift detection method is integrated to detect change in data pattern in data stream. The experimental results showed the modified ELM algorithm improves the accuracy of classification as well as can adapt to new concepts in a very short period of time. Experiments were carried out with Agarwal data set. Comparative analysis between Extreme learning machine (ELM), Online Sequential ELM (OS-ELM) and the Modified deep ELM shows that Modified deep ELM significantly improves the classification accuracy, robustness and stability and offer reliable solutions for real time data stream applications.