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Imbalanced Learning is a subset of Artificial Intelligence (AI) that focuses on the development of algorithms and models to address data sets that are imbalanced. This type of data set is characterized by a disproportionate ratio of one class of data to another, such as a large number of negative examples compared to a small number of positive examples. Imbalanced Learning algorithms are designed to address this issue by providing a more accurate representation of the data set.
Imbalanced Learning is used in a variety of applications, such as fraud detection, medical diagnosis, and customer segmentation. It is also used in natural language processing, computer vision, and robotics.
Companies in the Imbalanced Learning market include Microsoft, IBM, Google, Amazon, and Apple. Other companies include Oracle, SAP, Salesforce, and Adobe. Show Less Read more
