What would you do if you had a highly imbalanced dataset in a classification problem?

What would you do if you had a highly imbalanced dataset in a classification problem?

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 do you deal with Multilabel classification?

Basically, there are three methods to solve a multi-label classification problem, namely: Problem Transformation. Adapted Algorithm….

  1. 1 Binary Relevance. This is the simplest technique, which basically treats each label as a separate single class classification problem.
  2. 2 Classifier Chains.
  3. 3 Label Powerset.

What is the imbalanced class problem?

An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed. Many real-world classification problems have an imbalanced class distribution, such as fraud detection, spam detection, and churn prediction.

What is multiclass Multilabel classification?

Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.”

How to handle class imbalance in multi label classification?

We are attempting to implement multi-label classification using CNN in pytorch. We have 8 labels and around 260 images using a 90/10 split for train/validation sets. The classes are highly imbalanced with the most frequent class occurring in over 140 images. On the other hand, the least frequent class occurs in less than 5 images.

How to handle class imbalance in multi…?

The classes are highly imbalanced with the most frequent class occurring in over 140 images. On the other hand, the least frequent class occurs in less than 5 images. We attempted BCEWithLogitsLoss function initially that led to the model predicting the same label for all images.

How to deal with imbalanced multilabel scene classification?

The whole code was run on Google Colab, so a few paths and linux commands need to changed before running on a local machine. To deal with the Multilabel problem, we create a separate column for the labels obtained from Label Powerset transformation of the original labels. After this step the DataFrame data_df looks like this

Which is an example of an imbalanced classification task?

Generally, in an imbalanced classification task, the degree of imbalance can range from slight imbalance to severe imbalance, like in cases where there are only 1 example in a class. This type of tasks are very challenging and often the minority class is the more important class.