# Active Learning for High-Dimensional Binary Features

## TL;DR

This paper introduces an active learning strategy tailored for high-dimensional binary features, specifically applied to modeling EDFA devices in optical networks, improving prediction accuracy and reducing data labeling costs.

## Contribution

It proposes a novel active learning approach for binary features that leverages sparse linear models to enhance efficiency and accuracy in EDFA device modeling.

## Key findings

- Improved prediction accuracy on EDFA data
- Reduced labeling effort through active learning
- Effective performance demonstrated on simulated and real data

## Abstract

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.