Most existing classification approaches assume the underlying training set is evenly distributed but many real-world classification problems have an imbalanced class distribution, such as rare disease identification, fraud detection, spam detection, churn prediction, electricity theft & pilferage etc. These types of problems are also known as rare event prediction or extreme event prediction.

The training set for one class (majority) far surpasses the training set of the other class (minority), in which, the minority class is often the more interesting class.

In this project we,

• Review the issues that come with learning from imbalanced data sets and various problems in class-imbalance classification.
• Discuss some data level as well as algorithm level approaches for rare event prediction.

Link to the paper

• Most standard algorithms are accuracy driven, however, in a class imbalance dataset, accuracy tells very little about the minority class.
• Lack of information caused by small sample size.

• Suppose class A is 99% of our data-set and class B is the other 1%, but we are most interested in identifying instances of class B.
• We can reach an accuracy of 90% by simply predicting class A every time, but this provides a useless classifier for our intended use case.
• Instead, a properly calibrated method may achieve a lower accuracy, but would have a substantially higher true positive rate (or recall), which is really the metric we want to optimize for.

Generally, this problem deals with the trade-off between recall (percent of truly positive instances that were classified as such) and precision (percent of positive classifications that are truly positive).

We have chosen a dataset that contains transactions made by credit cards in September 2013 by European cardholders. Link to the dataset

• This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions.
• The dataset is highly imbalanced, the positive class (frauds) account for 0.172% of all transactions.
• It contains only numerical input variables which are the result of a PCA transformation (due to confidentiality issues).
• Feature 'Class' is the response variable, and it takes value 1 in case of fraud and 0 otherwise.

• Undersampling (RUS)
• Oversampling (SMOTe)

• Cost Sensitive Learning
• Ensemble Learning
• One Class Learning
• Hybrid Methods

• Recall of the minority class
• ROC_AUC score
• F1-Score
• Precision