The bagging technique is used in many different disciplines and provides perceptions for both useful applications and intriguing perspectives. Important use cases include:
Healthcare: Bagging has been used to make accurate predictions based on the medical data. For instance, studies show that ensemble techniques have been used to tackle a range of bioinformatics problems, such as choosing gene and/or proteins to find a particular trait of interest. This research concentrates more on using a variety of risk indicators to predict the onset of diabetes.
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IT: Bagging also improves the accuracy and precision of IT systems, such as network intrusion detection systems. This study looks at how bagging can improve network intrusion detection accuracy while reducing the frequency of false positives.
Environment: Ensemble techniques, like bagging, have been used in the field of remote sensing data. This study shows how it was used to map the various wetlands types in a coastal region.
Finance: Inside the financial industry, deep learning models and bagging have been used to automate critical tasks like identifying fraud, determining credit risk, and fixing problems with option pricing. This study (link leaves IBM) demonstrates the application of bagging as well as other machine learning techniques to assess the probability of loan default. By avoiding credit card theft, this study (link leaves IBM) reveals how bagging lowers risk in banks and other financial organisations.