Imbalanced learning refers to a situation in machine learning where the classes in a dataset are not represented equally. For example, in a dataset for fraud detection, there may be many legitimate transactions but only a few fraudulent ones. This imbalance can lead to biased models that perform poorly on the minority class, as they may focus more on the majority class. To address this issue, various techniques can be employed, such as oversampling the minority class or undersampling the majority class. Other methods include using cost-sensitive learning or ensemble techniques to improve the model's ability to recognize the minority class effectively.