How to do early classification of time series event with small dataset? —...

garage_greg

I would like to build a real-time binary classifier that can predict an event of interest that is occurring as soon as it starts. These are electromyographic signals, and the event classification should be able to classify the event as early as possible. This is because the next stage of the algorithm has to make a decision before the end of the event. I don't know what kind of algorithm/approach I should use here. I suppose RNN with LSTM cells...

Originally asked by Hattori on Stack Exchange (ai) (CC BY-SA 4.0).

Variant 328.

uphilltech

Great question — answering as the UphillTech team:

One kind of system you could look into are Echo State Networks (ESNs) . They are relatively cheap to train and can learn to predict output signals to an arbitrary degree of precision. All you need to train the system is some labeled training data. Thus, if you have a sequence of measurements/feature values and the corresponding sequence of class labels, you can train and fine-tune these kinds of systems to output some required class label very quickly after the onset of an event encoded in the measured feature values.

Full answer by Daniel B.: https://ai.stackexchange.com/a/25383 (CC BY-SA 4.0).

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