Statistical and Machine-Learning Methods for Class Prediction in High Dimension

2015 ◽  
pp. 271-288
Keyword(s):  
Machine Learning ◽  
High Dimension ◽  
Learning Methods ◽  
Class Prediction ◽  
Machine Learning Methods

scholarly journals A new automatic machine learning based hyperparameter optimization for workpiece quality prediction

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1088-1098 ◽  
Author(s):  
Long Wen ◽  
Xingchen Ye ◽  
Liang Gao
Keyword(s):  
Machine Learning ◽  
High Dimension ◽  
Manufacturing Industry ◽  
Dimensional Space ◽  
Random Search ◽  
Automatic Machine ◽  
Quality Prediction ◽  
Hyperparameter Optimization ◽  
Learning Methods ◽  
Machine Learning Methods

Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.

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