Understanding SMOTE: A Method for Balancing Classes in R

SMOTE (Synthetic Minority Over-sampling Technique) is a popular algorithm used in machine learning to balance the classes in a dataset. In this article, we will delve into the details of SMOTE and how it can be applied to balance over 200 classes in R.

Introduction to Class Imbalance

Class imbalance occurs when one class has a significantly larger number of instances than other classes in a dataset. This can lead to biased models that perform poorly on minority classes. In the given problem, we have a two-column dataset with over 200 classes and varying occurrences ranging from 1 to thousands.

Understanding SMOTE

SMOTE is an oversampling technique that generates new synthetic cases for the minority class by taking into account their nearest neighbors. This approach aims to increase the size of the minority class without increasing the overall size of the dataset.

The SMOTE Procedure

The SmoteClassif function in R’s UBL package combines two techniques: random undersampling and oversampling using SMOTE.

1. Random Undersampling: Randomly selects cases from the majority classes to reduce their size.
2. Oversampling using SMOTE: Generates new synthetic cases for the minority class by taking into account their nearest neighbors.

The goal is to obtain a new balanced dataset that has roughly the same size as the original dataset.

Applying SMOTE

To apply SMOTE, we need to specify two parameters:

• C.perc: The proportion of data points in each category to be retained. If set to NULL, then all samples from each class are kept.
• dist: The distribution used for generating synthetic cases. The default is “knn”.

SMOTE Example

The given example demonstrates how to use the SmoteClassif function:

# Load required libraries
library(MASS)

# Create a sample dataset with 200 classes and varying occurrences
data(cats)
table(cats$Sex)

F   M 
47 97 

# Apply SMOTE with k=1 (nearest neighbors computation in this bump)
mysmote.cats <- SmoteClassif(Sex~., cats, list(M = 2))
table(mysmote.cats$Sex)

F   M 
 47 194 

#class M is oversampled by 150% and class F is undersampled by 50%
mysmote.cats <- SmoteClassif(Sex~., cats, list(M = 1.5, F=0.5))
table(mysmote.cats$Sex)

F   M 
 23 145 

Understanding the Warnings

The warnings mentioned in the original problem arise when the selected value of k is too large to determine that specific number of neighbors for a particular case.

For instance, if we only have 3 examples of class A and select a case from this class, we will be able to find at most 2 nearest neighbors from that class. This can cause issues with the SMOTE algorithm, leading to warnings being displayed.

Best Practices

To avoid these warnings, it is essential to choose an appropriate value for k based on the size of each class and the distribution of instances within each class.

For example, we could use a smaller value of k when dealing with classes that have fewer instances:

# Apply SMOTE with k=1 (nearest neighbors computation in this bump)
mysmote.cats <- SmoteClassif(Sex~., cats, list(M = 2))
table(mysmote.cats$Sex)

F   M 
 47 194 

#class M is oversampled by 150% and class F is undersampled by 50%
mysmote.cats <- SmoteClassif(Sex~., cats, list(M = 1.5, F=0.5))
table(mysmote.cats$Sex)

F   M 
 23 145 

By applying these best practices and choosing an appropriate value for k, we can avoid the warnings and ensure that our SMOTE algorithm is functioning correctly.

Conclusion

SMOTE is a powerful technique for balancing classes in machine learning datasets. By understanding how it works and selecting an appropriate value for k based on class distribution, we can effectively balance the dataset and improve model performance on minority classes.

In this article, we have delved into the details of SMOTE and provided examples of its application in R using the UBL package. We have also discussed common warnings and best practices to avoid them. By following these guidelines, you can harness the power of SMOTE to create more balanced datasets and improve model performance.

Last modified on 2023-11-04