I'm using an SVM for an online classification, i.e. datapoints are classified as they come in. Of course, the training has occurred earlier, offline, with an annotated dataset.
However, the system has causality, i.e. each incoming datapoint is dependent on previous datapoints (they come from a sensor after all), so ideally, this should be taken into account for the classification.
Any advice on how I can incorporate that into my classification method? For now, no history is taken into account. Thanks!
Edit: By 'causality' I mean that the class-to-be-predicted is dependent on the previous class, as the physical system cannot jump from one state(=class) to another randomly, but some 'events' must happen, and hopefully these 'events' are captured in the features of the previous datapoints. The usage of the previous result of the classifier is also an option, although it may lead into a very rapidly accumulating error.