Hi there, is everything cool? Edited Nearest Neighbors Rule for undersampling involves using K 3 nearest neighbors to the data points that are misclassified and that are then removed before a K 1 classification rule is applied. This approach of resampling and classification was first proposed by Dennis Wilson in his 1972 paper titled "Asymptotic Properties of Nearest Neighbor Rules Using Edited Data." When used as an undersampling procedure, the rule can be applied to each example in the majority class, allowing those examples that are misclassified as belonging to the minority class to be removed and those correctly classified to remain. Let's see how can we apply the ENN And just like CNN, the ENN gives the best results when combined with another oversampling method like SMOTE.

In this article we're going to introduce the problem of dataset class imbalance which often occurs in real-world classification problems. We'll then look at oversampling as a possible solution and provide a coded example as a demonstration on an imbalanced dataset. Let's assume we have a dataset where the data points are classified into two categories: Class A and Class B. In an ideal scenario the division of the data point classifications would be equal between the two categories, e.g.: With the above scenario we could sufficiently measure the performance of a classification model using classification accuracy. Let's say in this case that Class B represents the suspect categories, e.g. a weapon/disease/fraud has been detected. If a model scored a classification accuracy of 90%, we may decide we're happy.