An extreme learning machine algorithm for semi-supervised classification of unbalanced data streams with concept drift

Data streams are important sources of information nowadays, and with the popularization of mobile devices and sensor systems that collect all kinds of data, more and more information is generated at an ever increasing speed. This growth in data supply poses some problems for traditional machine learning algorithms. Tasks such as data classification, regression, or data clustering presents some limitations regarding very large datasets, data streams, or variations in data. The high cost of labeling instances for training classification algorithms makes it difficult to use fully supervised algorithms. Unbalanced datasets tend to cause algorithms to ignore one or more classes. Moreover, concept drifts in data streams require algorithms to be retrained from time to time. In order to tackle such problems mentioned, a semi-supervised and online algorithm based on Extreme Learning Machine (ELM) called SSOE-FP-ELM is proposed and detailed. Experimental results show that the proposed algorithm outperform others in the literature in accuracy, generalization ability and concept drift detection and recovery, showing suitable alternatives for data streams classification.