@Article{jai.2021.016706,
AUTHOR = {Kunkun Wang, Xianda Liu},
TITLE = {An Anomaly Detection Method of Industrial Data Based on Stacking  Integration},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {9--19},
URL = {http://www.techscience.com/jai/v3n1/42097},
ISSN = {2579-003X},
ABSTRACT = {With the development of Internet technology, the computing power of 
data has increased, and the development of machine learning has become faster 
and faster. In the industrial production of industrial control systems, quality 
inspection and safety production of process products have always been our 
concern. Aiming at the low accuracy of anomaly detection in process data in 
industrial control system, this paper proposes an anomaly detection method based 
on stacking integration using the machine learning algorithm. Data are collected 
from the industrial site and processed by feature engineering. Principal component 
analysis (PCA) and integrated rule tree method are adopted to reduce the 
dimension of the process data, which can restore the original feature information 
of the data to the maximum extent. Random forest (RF), Adaboost, XGboost, 
SVM were selected as the first layer of basic learners. Logistic regression (LR) 
was used as the secondary learner to build the exception detection model based on 
stacking integrated method. TE data was used to train the base learner model and 
the integrated model. By comparing and analyzing the experimental results of
between integrated model and each basic learning model. By comparing and 
analyzing the experimental results of the constructed anomaly detection model and 
the basic learning model, the accuracy of process data anomaly detection is 
effectively improved, and the false alarm rate of process data anomaly detection 
is effectively reduced.},
DOI = {10.32604/jai.2021.016706}
}