An ensemble learning bagging prediction algorithm for classification, regression, and other tasks that operates by constructing a multitude of decision trees. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned.
Published in Chapter:
Aircraft Maintenance Prediction Tree Algorithms
Dima Alberg (Shamoon College of Engineering, Israel) and Yossi Hadad (Shamoon College of Engineering, Israel)
Copyright: © 2023
|Pages: 13
DOI: 10.4018/978-1-7998-9220-5.ch120
Abstract
The operation and maintenance of modern aircraft multi-sensor data fusion systems generate vast amounts of numerical and symbolic data. Learning useful and non-trivial insights from this data may lead to considerable savings, and detection and reduction of the number of faults, as a result increasing the overall level of aircraft safety. Several machine learning techniques exist to learn from big amounts of data. However, the use of these techniques to infer the desired readable and accurate interval regression tree models from the data obtained during the operation and maintenance of aircraft is extremely challenging. Difficulties that need to be addressed include data warehouse collection and preprocessing, machine learning model readability, setup, evaluation, and maintenance. This article presents the interval gradient prediction tree algorithm (INGPRET), which addresses these issues. As shown by the empirical evaluation of a real aircraft multi-sensor data set, the INGPRET algorithm provides better readability and similar performance in comparison to other machine learning algorithms.