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What is Class Imbalance

Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
When the observations or data points associated with an event is rare as compared to the data points or observations associated with the non-event, then the situation is called an imbalanced class. Typically, the data distribution percentage of data points in such cases range from 99:1 to 70:30 between majority and minority class respectively. The data which is available for typical classification problems like fraud detection and default prediction have such severe class imbalance.
Published in Chapter:
Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings
Siddharth Vinod Jain (Liverpool John Moores University, UK) and Manoj Jayabalan (Liverpool John Moores University, UK)
DOI: 10.4018/978-1-7998-8455-2.ch011
Abstract
The credit card has been one of the most successful and prevalent financial services being widely used across the globe. However, with the upsurge in credit card holders, banks are facing a challenge from equally increasing payment default cases causing substantial financial damage. This necessitates the importance of sound and effective credit risk management in the banking and financial services industry. Machine learning models are being employed by the industry at a large scale to effectively manage this credit risk. This chapter presents the application of the various machine learning methods like time series models and deep learning models experimented in predicting the credit card payment defaults along with identification of the significant features and the most effective evaluation criteria. This chapter also discusses the challenges and future considerations in predicting credit card payment defaults. The importance of factoring in a cost function to associate with misclassification by the models is also given.
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Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection
Scenario in which the number of observations belonging to one class is significantly lower than the number of observations belonging to the other classes.
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