Predicting rare failure events using classification trees on large scale manufacturing data with complex interactions

Hebert, J. O.

doi:10.1109/bigdata.2016.7840825

Cited by 13 publications

(15 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hebert [72] Experimental results show that certain tree-based classifiers, such as RF and XGBoost, are better learners than logistic regression Rio et al [46] The results of this study indicate that the classification performance of sampling methods, such as ROS, RUS, and SMOTE, is strongly related to the number of mappers used within a MapReduce environment (Apache Hadoop). The type of Data-Level sampling technique does not appear to matter Baughman et al [74] The research work discusses the gamification of the DeepQA system for real-world use.…”

Section: Algorithm-level Approachesmentioning

confidence: 93%

“…An imbalanced class environment can also be created by rare failure events within large-scale manufacturing operations. Hebert [72] separately compared RF and XGBoost (two tree-based classification methods) with logistic regression to research this issue.…”

Section: Algorithm-level Methods For Class Imbalance In Big Datamentioning

confidence: 99%

“…The best overall value for Gini Index Mean Decrease of RF was 0.15 (Table 4, Appendix A2). The Gini Index measures impurity between different classes in a population [72]. A lower Gini Index indicates better class separation.…”

Section: Algorithm-level Methods For Class Imbalance In Big Datamentioning

confidence: 99%

See 2 more Smart Citations

A survey on addressing high-class imbalance in big data

et al. 2018

View full text Add to dashboard Cite

Any dataset with unequal distribution between its majority and minority classes can be considered to have class imbalance, and in real-world applications, the severity of class imbalance can vary from minor to severe (high or extreme). A dataset can be considered imbalanced if the classes, e.g., fraud and non-fraud cases, are not equally represented. The majority class makes up most of the dataset, whereas the minority class, with limited dataset representation, is often considered the class of interest. With real-world datasets, class imbalance should be expected. If the degree of class imbalance for the majority class is extreme, then a classifier may yield high overall prediction accuracy since the model is likely predicting most instances as belonging to the majority class. Such a model is not practically useful, since it is often the prediction performance of the class of interest (i.e., minority class) that is more important for the domain experts [1]. He and Garcia [2] suggest that a popular viewpoint held by academic researchers defines imbalanced data as data with a high-class imbalance between its two classes, stating that high-class imbalance is reflected when the majority-to-minority class ratio ranges from

show abstract

Section: Algorithm-level Approachesmentioning

confidence: 93%

Section: Algorithm-level Methods For Class Imbalance In Big Datamentioning

confidence: 99%

See 1 more Smart Citation

A survey on addressing high-class imbalance in big data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Three different types of data are available: numeric, categorical and time based data which are described as being both scaled and anonymised. The categorical data is extremely sparse and is excluded for this research as it was for the research in [10], [14]. Initially, this research focuses only on the numerical data as preliminary data exploration found it to be the most influential, therefore the time/date variables were not within the scope of our research for this paper.…”

Section: Manufacturing Case Studymentioning

confidence: 99%

“…Rare event predictions and creation of fault prognostic systems using alternative methods have been covered in [9], [10]. Wang et al has demonstrated the usage of BNs for modelling fault detection in a semiconductor manufacturing process [9], where both discrete and continuous variables representing relationships within a manufacturing dataset were deemed to be highly correlated.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian network based learning system for modelling faults in large-scale manufacturing

Carbery¹,

Woods²,

Marshall³

2018

2018 IEEE International Conference on Industrial Technology (ICIT)

View full text Add to dashboard Cite

Abstract-Manufacturing companies can benefit from the early prediction and detection of failures to improve their product yield and reduce system faults through advanced data analytics. Whilst an abundance of data on their processing systems exist, they face difficulties in using it to gain insights to improve their systems. Bayesian networks (BNs) are considered here for diagnosing and predicting faults in a large manufacturing dataset from Bosch. Whilst BN structure learning has been performed traditionally on smaller sized data, this work demonstrates the ability to learn an appropriate BN structure for a large dataset with little information on the variables, for the first time. This paper also demonstrates a new framework for creating an appropriate probabilistic model for the Bosch dataset through the selection of statistically important variables on the response; this is then used to create a BN network which can be used to answer probabilistic queries and classify products based on changes in the sensor values in the production process.

show abstract

A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction

Guo

Zhou

2022

Journal of Forecasting

View full text Add to dashboard Cite

Personal credit data usually contain a large number of features, some of which do not significantly contribute to the performance of default prediction models. Screening features through appropriate methods is essential to improve the efficiency of prediction models. However, little attention has been paid to feature selection methods in the area of personal loan default prediction. In this study, we employ random forest (RF), XGBoost, Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) as base algorithms of wrapper and embedded methods to select features and use these algorithms as classifiers to predict personal loan default. We find that when classical filter methods are used to select features, the number of selected features needs to be large enough to enable tree‐based classifiers to get their best performance. However, when the tree‐based algorithm is used to select features, it only needs to select a small number of features to deliver a satisfactory classification performance. AdaBoost, Chi2, and F‐score are found to be ideal feature selection methods in the area of personal credit default prediction. Moreover, we find that it is better to use different algorithms in feature selection and classification; AdaBoost and CatBoost perform the best among all classifiers.

show abstract

Predicting rare failure events using classification trees on large scale manufacturing data with complex interactions

Cited by 13 publications

References 2 publications

A survey on addressing high-class imbalance in big data

A survey on addressing high-class imbalance in big data

A Bayesian network based learning system for modelling faults in large-scale manufacturing

A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction

Contact Info

Product

Resources

About