Comparison of Machine Learning Models for Data-Driven Aircraft Icing Severity Evaluation
- 1 December 2021
- journal article
- research article
- Published by American Institute of Aeronautics and Astronautics (AIAA) in Journal of Aerospace Information Systems
- Vol. 18 (12), 973-977
- https://doi.org/10.2514/1.i011047
Abstract
No abstract availableFunding Information
- National Science Foundation (#1854815)
- Argonne National Laboratory (#ANL 0 J-60008-0019A)
This publication has 15 references indexed in Scilit:
- A framework for sensitivity analysis of decision treesCentral European Journal of Operations Research, 2017
- Ordinal Logistic RegressionPublished by Springer Science and Business Media LLC ,2015
- Numerical simulation of ice accretions on an aircraft wingAerospace Science and Technology, 2011
- The Elements of Statistical LearningPublished by Springer Science and Business Media LLC ,2009
- Aircraft Ice Accretion Prediction Based on Neural NetworksJournal of Aircraft, 2006
- Not So Naive Bayes: Aggregating One-Dependence EstimatorsMachine Learning, 2005
- Summarizing multiple aspects of model performance in a single diagramJournal of Geophysical Research: Atmospheres, 2001
- Random ForestsMachine Learning, 2001
- The Nature of Statistical Learning TheoryPublished by Springer Science and Business Media LLC ,2000
- Support-vector networksMachine Learning, 1995