Online sequential extreme learning machine with the increased classes

In this paper, we investigate the online learning problem in a specific open environment, i.e., the number of classes can be dynamically increased. Online sequential extreme learning machine (OS-ELM) is extended to address the problem of the increased classes. Specifically, two different increased classes scenarios are considered. The first scenario is that the new classes, which haven't appeared in the previous instances, emerge in the new received data. The other scenario is that in data stream, an old class is split into several new subclasses due to some specific reasons. For the first kind of scenario, OS-ELM is inserted an alternative output node which can be extended whenever the new class instances are received. While for the second kind of scenario, we adopt a hierarchical structure to adapt the new split classes. We adopt the simple experiments to show the effectiveness and feasibility of the proposed models.