no code implementations • 20 Feb 2023 • Anna Jenul, Henning Langen Stokmo, Stefan Schrunner, Mona-Elisabeth Revheim, Geir Olav Hjortland, Oliver Tomic
Determining the most informative features for predicting the overall survival of patients diagnosed with high-grade gastroenteropancreatic neuroendocrine neoplasms is crucial to improve individual treatment plans for patients, as well as the biological understanding of the disease.
1 code implementation • 21 Sep 2021 • Anna Jenul, Stefan Schrunner, Bao Ngoc Huynh, Runar Helin, Cecilia Marie Futsæther, Kristian Hovde Liland, Oliver Tomic
This work presents three methods pursuing distinct strategies to rank features in multiblock ANNs by their importance: (1) a composite strategy building on individual feature importance rankings, (2) a knock-in, and (3) a knock-out strategy.
2 code implementations • 30 Apr 2021 • Anna Jenul, Stefan Schrunner, Jürgen Pilz, Oliver Tomic
Feature selection represents a measure to reduce the complexity of high-dimensional datasets and gain insights into the systematic variation in the data.
no code implementations • 24 Mar 2021 • Stefan Schrunner, Michael Scheiber, Anna Jenul, Anja Zernig, Andre Kästner, Roman Kern
Since high data volume and complex data formats delivered in modern high-end production environments go beyond the scope of classical process control systems, more advanced tools involving machine learning are required to reliably recognize failure patterns.
2 code implementations • 27 Sep 2020 • Anna Jenul, Stefan Schrunner, Kristian Hovde Liland, Ulf Geir Indahl, Cecilia Marie Futsaether, Oliver Tomic
Furthermore, unlike established feature selectors, RENT provides valuable information for model interpretation concerning the identification of objects in the data that are difficult to predict during training.