Turkish Journal of Electrical Engineering and Computer Sciences




Online learning methods (OLM) have been gaining traction as a solution to classification problems because of rapid renewal and fast growth in volume of available data. ELM-based sequential learning (OS-ELM) is one of the most frequently used online learning methodologies partly due to fast training algorithm but suffers from inefficient use of its hidden layers due to the random assignment of the parameters of those layers. In this study, we propose an improved online learning model called online sequential constrained extreme learning machine (OS-CELM), which replaces the random assignment of those parameters with better generalization performance using the CELM method based on the distance between classes. We compare the performance and training times of OS-ELM, ELM, and the proposed models for four different data sets. The results indicate that the proposed model has better generalization and accuracy performance.


Machine learning, extreme learning machine (ELM), constrained extreme learning machine (CELM), online sequential CELM (OS-CELM)

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