Does class imbalance affect clustering?

Does class imbalance affect clustering?

Finding minority class examples effectively and accurately without losing overall performance is the objective of class imbalance learning. The fundamental issue to be resolved is that the clustering ability of most standard learning algorithms is significantly compromised by imbalanced class distributions.

What can we do when we have imbalanced classes when we perform data clustering?

A simple way to fix imbalanced data-sets is simply to balance them, either by oversampling instances of the minority class or undersampling instances of the majority class. This simply allows us to create a balanced data-set that, in theory, should not lead to classifiers biased toward one class or the other.

How do you deal with imbalanced data classification?

7 Techniques to Handle Imbalanced Data

1. Use the right evaluation metrics.
2. Resample the training set.
3. Use K-fold Cross-Validation in the right way.
4. Ensemble different resampled datasets.
5. Resample with different ratios.
6. Cluster the abundant class.
7. Design your own models.

How to create clusters in class imbalanced data?

In the first strategy, the number of clusters (i.e. k) is set to be equal to the number of data samples in the minority class (i.e. k = N ). Then, the k cluster centers (or centroids) are produced by the k -means algorithm over the M data samples in the majority class.

Which is the aim of a clustering analysis?

The aim of clustering analysis is to group similar objects (i.e. data samples) into the same clusters; the objects in different clusters are different in terms of their feature representations [16]. Therefore, using clustering analysis to undersample the majority class generates a number of clusters, with each cluster containing similar data.

How are clusters used to overcome the limitations of undersampling?

To overcome the limitations of undersampling, we propose replacing the random undersampling strategy with a clustering technique. The aim of clustering analysis is to group similar objects (i.e. data samples) into the same clusters; the objects in different clusters are different in terms of their feature representations [16].

How is the number of clusters in the majority class set?

Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers.