In artificial intelligence and machine learning, the term “minority class” refers to the class or category in a dataset that has significantly fewer examples compared to other classes. This concept is most commonly discussed in the context of classification problems, where data is divided into two or more groups (classes) and the goal is to correctly assign new instances to one of these groups. For example, in a medical dataset for disease detection, if only 5% of the patients have a rare disease while 95% are healthy, the “disease” class is the minority class.
The presence of a minority class is a key aspect of what is known as an “imbalanced dataset.” When datasets are imbalanced, machine learning algorithms can become biased toward predicting the majority class simply because it is more common. This leads to poor performance in identifying or predicting the minority class, which can be especially problematic in high-stakes applications such as fraud detection, medical diagnosis, or spam filtering, where the minority class often represents the critical outcome.
Evaluating model performance on imbalanced datasets requires more than just measuring overall accuracy. Standard accuracy metrics can be misleading because a model can achieve high accuracy by always predicting the majority class and ignoring the minority class altogether. Instead, metrics like precision, recall, F1-score, and area under the Precision–Recall curve (PR AUC) are better suited to understanding how well a model handles the minority class.
To address the challenges posed by minority classes, several strategies can be used. One approach is oversampling, where instances of the minority class are duplicated or synthetically generated to balance the dataset. Another approach is undersampling, where examples from the majority class are randomly removed. More advanced methods include generating synthetic minority examples using algorithms like SMOTE (Synthetic Minority Over-sampling Technique), or using ensemble methods that are specifically designed to be robust to class imbalance. Additionally, some algorithms allow for weighting classes differently during training, so mistakes on the minority class are penalized more heavily.
Understanding and addressing the minority class is crucial for building fair and effective AI systems. Failing to do so can result in models that overlook rare but important cases, leading to missed opportunities or, in the worst cases, harmful decisions. Data scientists and machine learning engineers must be careful to examine the class distribution in their data and use appropriate techniques to ensure the minority class receives proper attention during model development and evaluation.
