  Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device
used to boost the intensity of optical signals being carried through a fiber
optic communication system. A highly accurate EDFA model is important because
of its crucial role in optical network management and optimization. The input
channels of an EDFA device are treated as either on or off, hence the input
features are binary. Labeled training data is very expensive to collect for
EDFA devices, therefore we devise an active learning strategy suitable for
binary variables to overcome this issue. We propose to take advantage of sparse
linear models to simplify the predictive model. This approach simultaneously
improves prediction and accelerates active learning query generation. We show
the performance of our proposed active learning strategies on simulated data
and real EDFA data.
