An Insight into Rare Class Problem: Analysis and Potential Solutions

Maheshwari, Satyam; Jain, Rachna; Jadon, R. S.

doi:10.3844/jcssp.2018.777.792

Cited by 8 publications

(5 citation statements)

References 53 publications

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“…However, it can be readily observed from Figure 2 that the "Red" label heavily dominates the sampled dataset. This so-called "class imbalance problem" has significant adverse effects on any machine learning algorithm [23][24][25][26]. It leads the model to be skewed towards the majority class, creating bias and rendering the algorithm unable to adapt to the features of the minority classes [23,24].…”

Section: Data Preprocessingmentioning

confidence: 99%

Rapid Visual Screening Feature Importance for Seismic Vulnerability Ranking via Machine Learning and SHAP Values

Karampinis,

Iliadis,

Karabinis

2024

Applied Sciences

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Structures inevitably suffer damage after an earthquake, with severity ranging from minimal damage of nonstructural elements to partial or even total collapse, possibly with loss of human lives. Thus, it is essential for engineers to understand the crucial factors that drive a structure towards suffering higher degrees of damage in order for preventative measures to be taken. In the present study, we focus on three well-known damage thresholds: the Collapse Limit State, Ultimate Limit State, and Serviceability Limit State. We analyze the features obtained via Rapid Visual Screening to determine whether or not a given structure crosses these thresholds. To this end, we use machine learning to perform binary classification for each damage threshold, and use explainability to quantify the effect of each parameter via SHAP values (SHapley Additive exPlanations). The quantitative results that we obtain demonstrate the potential applicability of ML methods for recalibrating the computation of structural vulnerability indices using data from recent earthquakes.

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Section: Data Preprocessingmentioning

confidence: 99%

Rapid Visual Screening Feature Importance for Seismic Vulnerability Ranking via Machine Learning and SHAP Values

Karampinis,

Iliadis,

Karabinis

2024

Applied Sciences

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“…From these, in 2,525,847 cases, the excitation resulted in the overturning of the block, while in 3,256,553 it did not. Thus, the class ratio was approximately 56:44, i.e., the dataset did not suffer from the so-called class imbalance problem, which can severely impact the performance of ML algorithms [53,54]. Finally, the maximum seismic rocking rotation was measured, but only for those instances where the block did not overturn.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Machine Learning Algorithms for the Prediction of the Seismic Response of Rigid Rocking Blocks

Karampinis,

Bantilas,

Kavvadias

et al. 2023

Applied Sciences

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A variety of structural members and non-structural components, including bridge piers, museum artifacts, furniture, or electrical and mechanical equipment, can uplift and rock under ground motion excitations. Given the inherently non-linear nature of rocking behavior, employing machine learning algorithms to predict rocking response presents a notable challenge. In the present study, the performance of supervised ML algorithms in predicting the maximum seismic response of free-standing rigid blocks subjected to ground motion excitations is evaluated. As such, both regression and classification algorithms were developed and tested, aiming to model the finite rocking response and rocking overturn. From this point of view, it is essential to estimate the maximum rocking rotation and to efficiently classify its magnitude by successfully assigning respective labels. To this end, a dataset containing the response data of 1100 rigid blocks subjected to 15,000 ground motion excitations, was employed. The results showed high accuracy in both the classification (95% accuracy) and regression (coefficient of determination R2=0.89) tasks.

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“…The dataset collected from the commercial bank of Ethiopia was target class imbalanced. According to , Burez et al, (2009) and Maheshwari et al, (2017), the collected dataset must be balanced before training to the proposed model. The synthetic minority oversampling technique (SMOTE), works by nominating related records from the smaller class and altering them one column at a time by a random amount to balance the data.…”

Section: Class Imbalancementioning

confidence: 99%

Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia

Asfaw

2023

The Scientific Temper

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The number of service providers is increasing rapidly in every business. These days, there is plenty of options for customers in the banking sector when choosing where to put their money. As a result, customer churn and engagement have become one of the top issues for most of the banks. In this paper, a method to predict customer churn in a Bank using machine learning techniques, which is a branch of artificial intelligence, is proposed. The research promotes the exploration of the likelihood of churn by analyzing customer behavior. random forest (RF), logistic regression (LR), gradient boosting classifier (GBC), extreme gradient boosting classifier (EGBC), and light gradient boosting machine classifier (LGBCMC) are used in this study. Also, some feature selection methods have also been done to find the more relevant features and verify system performance. The experimentation was conducted on the churn modeling dataset from Kaggle. The results are compared to find an appropriate model with higher precision and predictability. As a result, using the Random Forest model after oversampling is better than other models in terms of accuracy. The experimental result shows that the Light Gradient Boosting Machine classifier outperformed with an accuracy of 98%, a precision of 97%, and a recall of 100%, with an AUC of 99% than other proposed supervised machine learning algorithms with balanced datasets across all evaluation metrics.

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An Insight into Rare Class Problem: Analysis and Potential Solutions

Cited by 8 publications

References 53 publications

Rapid Visual Screening Feature Importance for Seismic Vulnerability Ranking via Machine Learning and SHAP Values

Rapid Visual Screening Feature Importance for Seismic Vulnerability Ranking via Machine Learning and SHAP Values

Machine Learning Algorithms for the Prediction of the Seismic Response of Rigid Rocking Blocks

Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia

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