IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures
IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures
复制标题
IDA 2016 工业挑战赛：使用机器学习预测故障
DOI：
10.1007/978-3-319-46349-0_33
复制
发表时间：
2016
影响因子：
5.9
通讯作者：
M. Nascimento
中科院分区：
文献类型：
--
作者：
Camila Ferreira;M. Nascimento
本文介绍了IDA 2016工业挑战的解决方案，该挑战包括使用机器学习来预测车辆气压系统的特定组件是否面临迫在眉睫的故障。此问题被建模为分类问题，因为目标是确定未观察到的实例是否代表故障。我们评估了各种最新的分类算法，并研究了如何处理不平衡的数据集和大量的缺失数据。我们的实验表明，最好的分类器在成本方面比随机分类的基线解决方案好92.56%。
This paper presents solutions to the IDA 2016 Industrial Challenge which consists of using machine learning in order to predict whether a specific component of the Air Pressure System of a vehicle faces imminent failure. This problem is modelled as a classification problem, since the goal is to determine if an unobserved instance represents a failure or not. We evaluate various state-of-the-art classification algorithms and investigate how to deal with the imbalanced dataset and with the high amount of missing data. Our experiments showed that the best classifier was cost-wise 92.56 % better than a baseline solution where a random classification is performed.