Development of a service parts recommendation system using clustering and classification of machine learning
作者：
Highlights：
• Service parts recommendation method based on machine learning to increase service quality.
• Recommend service parts having high value index by considering the life span and cost.
• K-means for clustering and random forest for classification.
• Hyperparameter tuning method to improve prediction performance.
• Modified F1 score to select the optimal conditions for the classification decision.
摘要
•Service parts recommendation method based on machine learning to increase service quality.•Recommend service parts having high value index by considering the life span and cost.•K-means for clustering and random forest for classification.•Hyperparameter tuning method to improve prediction performance.•Modified F1 score to select the optimal conditions for the classification decision.
论文关键词：Classification,Clustering,Hyperparameter tuning,Machine learning (ML),Optimal conditions for classification decision,Random forest (RF)
论文评审过程：Received 17 March 2021, Revised 21 July 2021, Accepted 11 October 2021, Available online 16 October 2021, Version of Record 21 October 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116084
