Exploring Accuracy and Efficiency of Preventive Maintenance Prediction with Machine Learning Techniques

Wei Bin Kelvin Lim

Chapter 11 in Business Analytics:Progress on Applications in Asia Pacific, 2016, pp 286-324 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This study discusses various machine learning techniques that were explored to improve accuracy and efficiency in predictions. A variation of different categories of methods such as dimension reduction, features scaling, hyperplane optimization, other machine learning classifier, and ensemble learning on the dataset were carried out to understand the difference in performance on a set of metrics.The results show that the Gradient Boosting Machine (GBM) model is robust and effective in tackling this problem. In addition, information on the tradeoff between accuracy and efficiency, and suggestions to the challenges faced during the implementation, were also made.

Keywords: Business Analytics; Entrepreneurship; Big Data; Information Technology (search for similar items in EconPapers)
JEL-codes: L26 (search for similar items in EconPapers)
Date: 2016
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