What is a major drawback of basic majority voting classification in kNN?

In k-nearest neighbors (kNN) classification, a major drawback of basic majority voting is that classes with more frequent examples tend to dominate the prediction outcomes. When making a classification decision, the algorithm evaluates the majority class among the k nearest neighbors. If one or more classes are significantly more prevalent in the dataset, they are more likely to appear in the nearest neighbors, leading to biased predictions that favor these dominant classes.

This misrepresentation can skew the results, especially in imbalanced datasets where one class has many more instances than others. As a result, even if a minority class has some of the nearest neighbors, the sheer volume of the dominant class can overshadow these instances, causing the model to inaccurately reflect the true distribution of classes in the data. Thus, the majority voting mechanism in its basic form can fail to capture important patterns in minority classes, which potentially reduces the overall effectiveness and fairness of the classification system.

It's important to recognize this limitation, as it highlights the need for alternative approaches, such as weighting the votes or employing more sophisticated classification algorithms that can better handle imbalances in class distributions.