2021
Díez-Pastor, José Francisco; Val, Alain Gil; Veiga, Fernando; Bustillo, Andrés
High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning Journal Article
In: Measurement, vol. 168, no. 108328, 2021, ISSN: 0263-2241.
Abstract | Links | BibTeX | Tags: Bagging, Cutting taps, Imbalanced datasets, Quality assessment, SELECTED, Threading
@article{Díez-Pastor2021,
title = {High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning},
author = {José Francisco Díez-Pastor and Alain Gil Val and Fernando Veiga and Andrés Bustillo},
url = {https://www.sciencedirect.com/science/article/pii/S0263224120308654},
doi = {https://doi.org/10.1016/j.measurement.2020.108328},
issn = {0263-2241},
year = {2021},
date = {2021-01-15},
journal = {Measurement},
volume = {168},
number = {108328},
abstract = {Industrial threading processes that use cutting taps are in high demand. However, industrial conditions differ markedly from laboratory conditions. In this study, a machine-learning solution is presented for the correct classification of threads, based on industrial requirements, to avoid expensive manual measurement of quality indicators. First, quality states are categorized. Second, process inputs are extracted from the torque signals including statistical parameters. Third, different machine-learning algorithms are tested: from base classifiers, such as decision trees and multilayer perceptrons, to complex ensembles of classifiers especially designed for imbalanced datasets, such as boosting and bagging decision-tree ensembles combined with SMOTE and under-sampling balancing techniques. Ensembles demonstrated the lowest sensitivity to window sizes, the highest accuracy for smaller window sizes, and the greatest learning ability with small datasets. Fourth, the combination of models with both high Recall and high Precision resulted in a reliable industrial tool, tested on an extensive experimental dataset.},
keywords = {Bagging, Cutting taps, Imbalanced datasets, Quality assessment, SELECTED, Threading},
pubstate = {published},
tppubtype = {article}
}
Industrial threading processes that use cutting taps are in high demand. However, industrial conditions differ markedly from laboratory conditions. In this study, a machine-learning solution is presented for the correct classification of threads, based on industrial requirements, to avoid expensive manual measurement of quality indicators. First, quality states are categorized. Second, process inputs are extracted from the torque signals including statistical parameters. Third, different machine-learning algorithms are tested: from base classifiers, such as decision trees and multilayer perceptrons, to complex ensembles of classifiers especially designed for imbalanced datasets, such as boosting and bagging decision-tree ensembles combined with SMOTE and under-sampling balancing techniques. Ensembles demonstrated the lowest sensitivity to window sizes, the highest accuracy for smaller window sizes, and the greatest learning ability with small datasets. Fourth, the combination of models with both high Recall and high Precision resulted in a reliable industrial tool, tested on an extensive experimental dataset.
2015
Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César; Kuncheva, Ludmila I
Random Balance: Ensembles of variable priors classifiers for imbalanced data Journal Article
In: Knowledge-Based Systems, vol. 85, pp. 96-111, 2015, ISSN: 0950-7051.
Abstract | Links | BibTeX | Tags: AdaBoost, Bagging, Class-imbalanced problems, Classifier ensembles, Data Mining, Ensemble methods, SELECTED, SMOTE, Undersampling
@article{RandomBalance,
title = {Random Balance: Ensembles of variable priors classifiers for imbalanced data},
author = {José Francisco Díez-Pastor and Juan José Rodríguez and César García-Osorio and Ludmila I Kuncheva},
url = {http://www.sciencedirect.com/science/article/pii/S0950705115001720},
doi = {10.1016/j.knosys.2015.04.022},
issn = {0950-7051},
year = {2015},
date = {2015-01-01},
journal = {Knowledge-Based Systems},
volume = {85},
pages = {96-111},
abstract = {Abstract In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. Class-imbalanced problems sets arise routinely in many application domains and pose a challenge to traditional classifiers. We propose a new approach to building ensembles of classifiers for two-class imbalanced data sets, called Random Balance. Each member of the Random Balance ensemble is trained with data sampled from the training set and augmented by artificial instances obtained using SMOTE. The novelty in the approach is that the proportions of the classes for each ensemble member are chosen randomly. The intuition behind the method is that the proposed diversity heuristic will ensure that the ensemble contains classifiers that are specialized for different operating points on the ROC space, thereby leading to larger AUC compared to other ensembles of classifiers. Experiments have been carried out to test the Random Balance approach by itself, and also in combination with standard ensemble methods. As a result, we propose a new ensemble creation method called RB-Boost which combines Random Balance with AdaBoost.M2. This combination involves enforcing random class proportions in addition to instance re-weighting. Experiments with 86 imbalanced data sets from two well known repositories demonstrate the advantage of the Random Balance approach.},
keywords = {AdaBoost, Bagging, Class-imbalanced problems, Classifier ensembles, Data Mining, Ensemble methods, SELECTED, SMOTE, Undersampling},
pubstate = {published},
tppubtype = {article}
}
Abstract In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. Class-imbalanced problems sets arise routinely in many application domains and pose a challenge to traditional classifiers. We propose a new approach to building ensembles of classifiers for two-class imbalanced data sets, called Random Balance. Each member of the Random Balance ensemble is trained with data sampled from the training set and augmented by artificial instances obtained using SMOTE. The novelty in the approach is that the proportions of the classes for each ensemble member are chosen randomly. The intuition behind the method is that the proposed diversity heuristic will ensure that the ensemble contains classifiers that are specialized for different operating points on the ROC space, thereby leading to larger AUC compared to other ensembles of classifiers. Experiments have been carried out to test the Random Balance approach by itself, and also in combination with standard ensemble methods. As a result, we propose a new ensemble creation method called RB-Boost which combines Random Balance with AdaBoost.M2. This combination involves enforcing random class proportions in addition to instance re-weighting. Experiments with 86 imbalanced data sets from two well known repositories demonstrate the advantage of the Random Balance approach.
2012
Maudes, Jesús; Rodríguez, Juan José; García-Osorio, César; García-Pedrajas, Nicolás
Random Feature Weights for Decision Tree Ensemble Construction Journal Article
In: Information Fusion, vol. 13, no. 1, pp. 20-30, 2012, ISSN: 1566-2535.
Links | BibTeX | Tags: Bagging, Boosting, Classifier ensembles, Data Mining, Decision trees, Ensemble methods, Random forest
@article{RFW2012,
title = {Random Feature Weights for Decision Tree Ensemble Construction},
author = {Jesús Maudes and Juan José Rodríguez and César García-Osorio and Nicolás García-Pedrajas},
doi = {10.1016/j.inffus.2010.11.004},
issn = {1566-2535},
year = {2012},
date = {2012-01-01},
journal = {Information Fusion},
volume = {13},
number = {1},
pages = {20-30},
keywords = {Bagging, Boosting, Classifier ensembles, Data Mining, Decision trees, Ensemble methods, Random forest},
pubstate = {published},
tppubtype = {article}
}