Clustering-based adaptive data augmentation for class-imbalance in machine learning (CADA): additive manufacturing use case

Dasari, Siva Krishna; Cheddad, Abbas; Palmquist, Jonatan; Lundberg, Lars

doi:10.1007/s00521-022-07347-6

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…The data-level approach involves oversampling and undersampling techniques to maintain balance between classes before classification [18]. And this is what it was applied for in the AIRA-ML model, as shown in Fig 1, and the implementation is illustrated in Fig.…”

Section: A) Data Level Methodsmentioning

confidence: 99%

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AIRA-ML: Auto Insurance Risk Assessment-Machine Learning Model using Resampling Methods

Elbhrawy,

Belal,

Hassanein

2023

IJACSA

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Predicting underwriting risk has become a major challenge due to the imbalanced datasets in the field. A realworld imbalanced dataset is used in this work with 12 variables in 30144 cases, where most of the cases were classified as "accepting the insurance request", while a small percentage classified as "refusing insurance". This work developed 55 machine learning (ML) models to predict whether or not to renew policies. The models were developed using the original dataset and four data-level approaches resampling techniques: random oversampling, SMOTE, random undersampling, and hybrid methods with 11 ML algorithms to address the issue of imbalanced data (11 ML× (4 resampling techniques + unbalanced datasets) = 55 ML models).Seven classifier efficiency measures were used to evaluate these 55 models that were developed using 11 ML algorithms: logistic regression (LR), random forest (RF), artificial neural network (ANN), multilayer perceptron (MLP), support vector machine (SVM), naive Bayes (NB), decision tree (DT), XGBoost, k-nearest neighbors (KNN), stochastic gradient boosting (SGB), and AdaBoost. The seven classifier efficiency measures namely are accuracy, sensitivity, specificity, AUC, precision, F1-measure, and kappa. CRISP-DM methodology is utilisied to ensure that studies are conducted in a rigorous and systematic manner. Additionally, RapidMiner software was used to apply the algorithms and analyze the data, which highlighted the potential of ML to improve the accuracy of risk assessment in insurance underwriting. The results showed that all ML classifiers became more effective when using resampling strategies; where Hybrid resampling methods improved the performance of machine learning models on imbalanced data with an accuracy of 0.9967 and kappa statistics of 0.992 for the RF classifier.

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Section: A) Data Level Methodsmentioning

confidence: 99%

“… Under-Sampling Methods Under-sampling, also known as random under-sampling, is a technique to address unbalanced data by removing cases from the majority class of the training dataset [18].…”

Section: A) Data Level Methodsmentioning

confidence: 99%

AIRA-ML: Auto Insurance Risk Assessment-Machine Learning Model using Resampling Methods

Elbhrawy,

Belal,

Hassanein

2023

IJACSA

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“…Lastly, the oversampling technique can influence the results, including other would be beneficial, such as cluster-based adaptive data augmentation (CADA) [89], synthetic minority oversampling technique (SMOTE) [90] and other newer techniques, for example, MA-HAKIL [91].…”

Section: Future Workmentioning

confidence: 99%

Prediction of Dementia Based on Older Adults' Sleep Disturbances Using Machine Learning

Nyholm,

Ghazi,

Ghazi

et al. 2023

Preprint

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CAD-based data augmentation and transfer learning empowers part classification in manufacturing

Ruediger¹,

Glatt²,

Hussong³

et al. 2023

Int J Adv Manuf Technol

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Especially in manufacturing systems with small batches or customized products, as well as in remanufacturing and recycling facilities, there is a wide variety of part types that may be previously unseen. It is crucial to accurately identify these parts based on their type for traceability or sorting purposes. One approach that has shown promising results for this task is deep learning–based image classification, which can classify a part based on its visual appearance in camera images. However, this approach relies on large labeled datasets of real-world images, which can be challenging to obtain, especially for parts manufactured for the first time or whose appearance is unknown. To overcome this challenge, we propose generating highly realistic synthetic images based on photo-realistically rendered computer-aided design (CAD) data. Using this commonly available source, we aim to reduce the manual effort required for data generation and preparation and improve the classification performance of deep learning models using transfer learning. In this approach, we demonstrate the creation of a parametric rendering pipeline and show how it can be used to train models for a 30-class classification problem with typical engineering parts in an industrial use case. We also demonstrate how our method’s entropy gain improves the classification performance in various deep image classification models.

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Clustering-based adaptive data augmentation for class-imbalance in machine learning (CADA): additive manufacturing use case

Cited by 8 publications

References 28 publications

AIRA-ML: Auto Insurance Risk Assessment-Machine Learning Model using Resampling Methods

AIRA-ML: Auto Insurance Risk Assessment-Machine Learning Model using Resampling Methods

Prediction of Dementia Based on Older Adults' Sleep Disturbances Using Machine Learning

CAD-based data augmentation and transfer learning empowers part classification in manufacturing

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