SMOTE Generates Duplicate Samples for Majority Sample: Understanding the Concept and Its Implications

The Synthetic Minority Over-sampling Technique (SMOTE) is a popular oversampling technique used to balance the class distribution of a dataset, particularly for minority classes. However, in this article, we will delve into the reasons behind SMOTE generating duplicate samples for the majority sample (common class) and explore its implications on the accuracy of classification models.

Understanding SMOTE

SMOTE is an oversampling technique that aims to create synthetic minority class samples by interpolating between existing minority class instances. The technique uses a distance metric, such as Euclidean distance or Manhattan distance, to calculate the distances between instances in the majority and minority classes. A random subset of the majority class instances is selected, and for each instance in this subset, SMOTE generates multiple synthetic instances using interpolation.

Why Does SMOTE Generate Duplicate Samples?

SMOTE generates duplicate samples for the majority sample because it uses a random subset of the majority class instances to generate the synthetic minority class samples. This approach can lead to the creation of duplicate samples when the random selection includes instances that are already similar to each other.

To illustrate this, let’s consider an example where we have a dataset with 1000 instances, 500 from the majority class (common) and 500 from the minority class (rare). We apply SMOTE with a random subset size of 10% for the majority class. In this case, SMOTE will select 50 instances from the majority class and generate synthetic samples using interpolation.

However, if the selected instances are already similar to each other, SMOTE may create duplicate samples by interpolating between these instances. For example, suppose the randomly selected instance is x1, which is close to another instance x2. SMOTE will interpolate between x1 and x2 to create a synthetic instance that is also close to both x1 and x2.

Implications of Duplicate Samples

Duplicate samples can affect the accuracy of classification models in several ways:

1. Overfitting: Duplicate samples can lead to overfitting, as the model learns to fit the noise introduced by these duplicate samples.
2. Reduced Generalization Performance: The presence of duplicate samples can reduce the generalization performance of the model, as it may not generalize well to new, unseen data.
3. Biased Models: Duplicate samples can result in biased models that are more accurate for one class but less accurate for another.

Balancing the Class Distribution

To avoid generating duplicate samples and achieve a balanced dataset, it is essential to balance the class distribution using different techniques:

1. Undersampling: Undersample the majority class to create instances for the minority class.
2. Oversampling: Oversample the minority class to create more instances than those in the majority class.
3. SMOTE: Use SMOTE with a small perc.over value (e.g., 1) to oversample the minority class and generate synthetic samples.

Example Code: Balancing Iris Dataset

Let’s use an example to demonstrate how to balance the class distribution of an iris dataset using different techniques:

## A small example with a data set created artificially from the IRIS

data <- iris[, c(1, 2, 5)]
data$Species <- factor(ifelse(data$Species == "setosa","rare","common"))

## checking the class distribution of this artificial data set

table(data$Species)

## now using SMOTE to create a more balanced problem

newData <- performanceEstimation::smote(Species ~ ., data, perc.over = 1,perc.under=2, k =10)
table(newData$Species)

## Checking visually the created data

par(mfrow = c(1, 2))
plot(data[, 1], data[, 2], pch = 19 + as.integer(data[, 3]),
     main = "Original Data")
plot(newData[, 1], newData[, 2], pch = 19 + as.integer(newData[,3]),
     main = "SMOTE'd Data")

## Balancing using undersampling and oversampling

data_balanced <- data[iris$Species == "rare",]
newData_undersample <- performanceEstimation::smote(Species ~ ., data_balanced, perc.over = 1,perc.under=2, k =10)
table(newData_undersample$Species)

data_balanced <- data[iris$Species == "common",]
newData_oversample <- performanceEstimation::smote(Species ~ ., data_balanced, perc.over = 2,perc.under=1, k =10)
table(newData_oversample$Species)

Comparison of Different SMOTE Packages

There are several SMOTE packages available in R, each with its strengths and weaknesses:

• performanceEstimation::SMOTE: This package provides a simple and efficient way to balance the class distribution using SMOTE.
• DMwR::SMOTE: This package offers more advanced features, such as support for multiple metrics and the ability to create custom distance functions.
• smotefamily::SMOTE: This package provides an interface to other SMOTE algorithms, such as k-Nearest Neighbors (k-NN) and Local Outlier Factor (LOF).

Each package has its strengths and weaknesses, and the choice of which one to use depends on the specific requirements of your project.

Conclusion

In conclusion, SMOTE generates duplicate samples for the majority sample because it uses a random subset of the majority class instances to generate synthetic minority class samples. This can lead to overfitting, reduced generalization performance, and biased models. To avoid these issues, it is essential to balance the class distribution using different techniques, such as undersampling, oversampling, or SMOTE.

By understanding the concept behind SMOTE and its implications on classification models, you can make informed decisions about which SMOTE package to use and how to apply them to achieve a balanced dataset.

Last modified on 2025-03-03