Active Learning for High-Dimensional Binary Features
This work addresses the challenge of optical network management optimization for EDFA devices, but it is incremental as it applies existing active learning concepts to a specific domain with binary features.
The paper tackled the problem of expensive labeled data collection for Erbium-doped fiber amplifier (EDFA) devices with binary input features by devising an active learning strategy using sparse linear models, resulting in improved prediction and faster query generation as demonstrated on simulated and real data.
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.