Scalable real-time attributes responsive extreme learning machine

Abstract

Extreme learning machine (ELM) has recently attracted many researchers’ interest due to its very fast learning speed, and ease of implementation. Its many applications, such as regression, binary and multiclass classification, acquired better results. However, when some attributes of the dataset have been lost, this fixed network structure will be less than satisfactory. This article suggests a Scalable Real-Time Attributes Responsive Extreme Learning Machine (Star-ELM), which can grow its appropriate structure with nodes autonomous coevolution based on the different dataset. Its hidden nodes can be merged to more effectively adjust structure and weight. In the experiments of classical datasets we compare with other relevant variants of ELM, Star-ELM makes better performance on classification learning with loss of dataset attributes in some situations.