Disruption Prediction in Fusion Devices through Feature Extraction and
  Logistic Regression
Diogo R. Ferreira
This document describes an approach used in the Multi-Machine Disruption
Prediction Challenge for Fusion Energy by ITU, a data science competition which
ran from September to November 2023, on the online platform Zindi. The
competition involved data from three fusion devices - C-Mod, HL-2A, and J-TEXT
- with most of the training data coming from the last two, and the test data
coming from the first one. Each device has multiple diagnostics and signals,
and it turns out that a critical issue in this competition was to identify
which signals, and especially which features from those signals, were most
relevant to achieve accurate predictions. The approach described here is based
on extracting features from signals, and then applying logistic regression on
top of those features. Each signal is treated as a separate predictor and, in
the end, a combination of such predictors achieved the first place on the
leaderboard.