September 5th, 2016
(written by lawrence krubner, however indented passages are often quotes). You can contact lawrence at: firstname.lastname@example.org
Research on imbalanced classes often considers imbalanced to mean a minority class of 10% to 20%. In reality, datasets can get far more imbalanced than this. Here are some examples:
1.) About 2% of credit card accounts are defrauded per year1. (Most fraud detection domains are heavily imbalanced.)
2.) Medical screening for a condition is usually performed on a large population of people without the condition, to detect a small minority with it (e.g., HIV prevalence in the USA is ~0.4%).
3.) Disk drive failures are approximately ~1% per year.
4.) The conversion rates of online ads has been estimated to lie between 10-3 to 10-6.
5.) Factory production defect rates typically run about 0.1%.
Many of these domains are imbalanced because they are what I call needle in a haystack problems, where machine learning classifiers are used to sort through huge populations of negative (uninteresting) cases to find the small number of positive (interesting, alarm-worthy) cases.
When you encounter such problems, you’re bound to have difficulties solving them with standard algorithms. Conventional algorithms are often biased towards the majority class because their loss functions attempt to optimize quantities such as error rate, not taking the data distribution into consideration2. In the worst case, minority examples are treated as outliers of the majority class and ignored. The learning algorithm simply generates a trivial classifier that classifies every example as the majority class.
This might seem like pathological behavior but it really isn’t. Indeed, if your goal is to maximize simple accuracy (or, equivalently, minimize error rate), this is a perfectly acceptable solution. But if we assume that the rare class examples are much more important to classify, then we have to be more careful and more sophisticated about attacking the problem.