（转）8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset

8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset

by Jason Brownlee on August 19, 2015 in Machine Learning Process

Has this happened to you?

You are working on your dataset. You create a classification model and get 90% accuracy immediately. “Fantastic” you think. You dive a little deeper and discover that 90% of the data belongs to one class. Damn!

This is an example of an imbalanced dataset and the frustrating results it can cause.

In this post you will discover the tactics that you can use to deliver great results on machine learning datasets with imbalanced data.

Find some balance in your machine learning.
Photo by MichaEli, some rights reserved.

Coming To Grips
With Imbalanced Data

I get emails
about class imbalance all the time, for example:

I have a binary
classification problem and one class is present with 60:1 ratio in my training
set. I used the logistic regression and the result seems to just ignores one
class.

And this:

I am working on
a classification model. In my dataset I have three different labels to be
classified, let them be A, B and C. But in the training dataset I have A
dataset with 70% volume, B with 25% and C with 5%. Most of time my results are
overfit to A. Can you please suggest how can I solve this problem?

I write long
lists of techniques to try and think about the best ways to get past this
problem. I finally took the advice of one of my students:

Perhaps one of
your upcoming blog posts could address the problem of training a model to
perform against highly imbalanced data, and outline some techniques and
expectations.

Frustration!

Imbalanced data
can cause you a lot of frustration.

You feel very
frustrated when you discovered that your data has imbalanced classes and that
all of the great results you thought you were getting turn out to be a lie.

The next wave of
frustration hits when the books, articles and blog posts don’t seem to give you
good advice about handling the imbalance in your data.

Relax, there are
many options and we’re going to go through them all. It is possible, you can
build predictive models for imbalanced data.

What is
Imbalanced Data?

Imbalanced data
typically refers to a problem with classification problems where the classes
are not represented equally.

For example, you
may have a 2-class (binary) classification problem with 100 instances (rows). A
total of 80 instances are labeled with Class-1 and the remaining 20 instances
are labeled with Class-2.

This is an
imbalanced dataset and the ratio of Class-1 to Class-2 instances is 80:20 or
more concisely 4:1.

You can have a
class imbalance problem on two-class classification problems as well as
multi-class classification problems. Most techniques can be used on either.

The remaining
discussions will assume a two-class classification problem because it is easier
to think about and describe.

Imbalance is
Common

Most
classification data sets do not have exactly equal number of instances in each
class, but a small difference often does not matter.

There are
problems where a class imbalance is not just common, it is expected. For
example, in datasets like those that characterize fraudulent transactions are
imbalanced. The vast majority of the transactions will be in the “Not-Fraud”
class and a very small minority will be in the “Fraud” class.

Another example
is customer churn datasets, where the vast majority of customers stay with the
service (the “No-Churn” class) and a small minority cancel their subscription
(the “Churn” class).

When there is a
modest class imbalance like 4:1 in the example above it can cause problems.

Accuracy Paradox

The accuracy paradox is the
name for the exact situation in the introduction to this post.

It is the case
where your accuracy measures tell the story that you have excellent accuracy
(such as 90%), but the accuracy is only reflecting the underlying class
distribution.

It is very
common, because classification accuracy is often the first measure we use when
evaluating models on our classification problems.

Put it All On
Red!

What is going on
in our models when we train on an imbalanced dataset?

As you might
have guessed, the reason we get 90% accuracy on an imbalanced data (with 90% of
the instances in Class-1) is because our models look at the data and cleverly
decide that the best thing to do is to always predict “Class-1” and achieve
high accuracy.

This is best
seen when using a simple rule based algorithm. If you print out the rule in the
final model you will see that it is very likely predicting one class regardless
of the data it is asked to predict.

8 Tactics To
Combat Imbalanced Training Data

We now
understand what class imbalance is and why it provides misleading
classification accuracy.

So what are our
options?

1) Can
You Collect More Data?

You might think
it’s silly, but collecting more data is almost always overlooked.

Can you collect
more data? Take a second and think about whether you are able to gather more
data on your problem.

A larger dataset
might expose a different and perhaps more balanced perspective on the classes.

More examples of
minor classes may be useful later when we look at resampling your dataset.

2) Try Changing
Your Performance Metric

Accuracy is not
the metric to use when working with an imbalanced dataset. We have seen that it
is misleading.

There are
metrics that have been designed to tell you a more truthful story when working
with imbalanced classes.

I give more advice on selecting
different performance measures in my post “Classification Accuracy is Not Enough: More
Performance Measures You Can Use“.

In that post I
look at an imbalanced dataset that characterizes the recurrence of breast
cancer in patients.

From that post,
I recommend looking at the following performance measures that can give more
insight into the accuracy of the model than traditional classification
accuracy:

• Confusion Matrix: A breakdown of predictions into a table showing correct
  predictions (the diagonal) and the types of incorrect predictions made (what
  classes incorrect predictions were assigned).
• Precision: A measure of a classifiers exactness.
• Recall: A measure of a classifiers completeness
• F1 Score (or F-score): A weighted average of precision and recall.

I would also
advice you to take a look at the following:

• Kappa (or Cohen’s kappa): Classification accuracy normalized by the imbalance of
  the classes in the data.
• ROC Curves: Like precision and recall, accuracy is divided into
  sensitivity and specificity and models can be chosen based on the balance
  thresholds of these values.

You can learn a lot more about using ROC
Curves to compare classification accuracy in our post “Assessing and Comparing Classifier
Performance with ROC Curves“.

Still not sure?
Start with kappa, it will give you a better idea of what is going on than
classification accuracy.

3) Try
Resampling Your Dataset

You can change
the dataset that you use to build your predictive model to have more balanced
data.

This change is
called sampling your dataset and there are two main methods that you can use to
even-up the classes:

1. You can add copies
   of instances from the under-represented class called over-sampling (or more
   formally sampling with replacement), or
2. You can delete
   instances from the over-represented class, called under-sampling.

These approaches
are often very easy to implement and fast to run. They are an excellent
starting point.

In fact, I would
advise you to always try both approaches on all of your imbalanced datasets,
just to see if it gives you a boost in your preferred accuracy measures.

You can learn a little more in the the
Wikipedia article titled “Oversampling and undersampling in data
analysis“.

Some Rules of
Thumb

• Consider testing
  under-sampling when you have an a lot data (tens- or hundreds of thousands of
  instances or more)
• Consider testing
  over-sampling when you don’t have a lot of data (tens of thousands of records
  or less)
• Consider testing
  random and non-random (e.g. stratified) sampling schemes.
• Consider testing
  different resampled ratios (e.g. you don’t have to target a 1:1 ratio in a
  binary classification problem, try other ratios)

4) Try Generate
Synthetic Samples

A simple way to
generate synthetic samples is to randomly sample the attributes from instances
in the minority class.

You could sample
them empirically within your dataset or you could use a method like Naive Bayes
that can sample each attribute independently when run in reverse. You will have
more and different data, but the non-linear relationships between the
attributes may not be preserved.

There are
systematic algorithms that you can use to generate synthetic samples. The most
popular of such algorithms is called SMOTE or the Synthetic Minority
Over-sampling Technique.

As its name
suggests, SMOTE is an oversampling method. It works by creating synthetic
samples from the minor class instead of creating copies. The algorithm selects
two or more similar instances (using a distance measure) and perturbing an
instance one attribute at a time by a random amount within the difference to
the neighboring instances.

Learn more about SMOTE, see the original
2002 paper titled “SMOTE: Synthetic Minority Over-sampling Technique“.

There are a
number of implementations of the SMOTE algorithm, for example:

• In Python, take a
  look at the “UnbalancedDataset” module. It provides a number of
  implementations of SMOTE as well as various other resampling techniques that
  you could try.
• In R, the DMwR package provides an implementation of SMOTE.
• In Weka, you can
  use the SMOTE supervised filter.

5) Try Different
Algorithms

As always, I
strongly advice you to not use your favorite algorithm on every problem. You
should at least be spot-checking a variety of different types of algorithms on
a given problem.

For more on
spot-checking algorithms, see my post “Why you should be Spot-Checking
Algorithms on your Machine Learning Problems”.

That being said,
decision trees often perform well on imbalanced datasets. The splitting rules
that look at the class variable used in the creation of the trees, can force
both classes to be addressed.

If in doubt, try
a few popular decision tree algorithms like C4.5, C5.0, CART, and Random
Forest.

For some example R code using decision
trees, see my post titled “Non-Linear Classification in R with Decision
Trees“.

For an example of using CART in Python
and scikit-learn, see my post titled “Get Your Hands Dirty With Scikit-Learn Now“.

6) Try Penalized
Models

You can use the same
algorithms but give them a different perspective on the problem.

Penalized
classification imposes an additional cost on the model for making
classification mistakes on the minority class during training. These penalties
can bias the model to pay more attention to the minority class.

Often the
handling of class penalties or weights are specialized to the learning
algorithm. There are penalized versions of algorithms such as penalized-SVM and
penalized-LDA.

It is also possible to have generic
frameworks for penalized models. For example, Weka has a CostSensitiveClassifier that can wrap any
classifier and apply a custom penalty matrix for miss classification.

Using
penalization is desirable if you are locked into a specific algorithm and are
unable to resample or you’re getting poor results. It provides yet another way
to “balance” the classes. Setting up the penalty matrix can be complex. You
will very likely have to try a variety of penalty schemes and see what works
best for your problem.

7) Try a
Different Perspective

There are fields
of study dedicated to imbalanced datasets. They have their own algorithms,
measures and terminology.

Taking a look
and thinking about your problem from these perspectives can sometimes shame
loose some ideas.

Two you might like to consider are anomaly detection and change detection.

Anomaly detection is the detection
of rare events. This might be a machine malfunction indicated through its
vibrations or a malicious activity by a program indicated by it’s sequence of
system calls. The events are rare and when compared to normal operation.

This shift in
thinking considers the minor class as the outliers class which might help you
think of new ways to separate and classify samples.

Change detection is similar
to anomaly detection except rather than looking for an anomaly it is looking
for a change or difference. This might be a change in behavior of a user as
observed by usage patterns or bank transactions.

Both of these
shifts take a more real-time stance to the classification problem that might
give you some new ways of thinking about your problem and maybe some more
techniques to try.

8) Try Getting
Creative

Really climb
inside your problem and think about how to break it down into smaller problems
that are more tractable.

For inspiration, take a look at the very
creative answers on Quora in response to the question “In classification, how do you handle an
unbalanced training set?”

For example:

Decompose your
larger class into smaller number of other classes…

…use a One Class
Classifier… (e.g. treat like outlier detection)

…resampling the
unbalanced training set into not one balanced set, but several. Running an
ensemble of classifiers on these sets could produce a much better result than
one classifier alone

These are just a
few of some interesting and creative ideas you could try.

For more ideas, check out these comments
on the reddit post “Classification when 80% of my training set is
of one class“.

Pick a Method
and Take Action

You do not need
to be an algorithm wizard or a statistician to build accurate and reliable
models from imbalanced datasets.

We have covered
a number of techniques that you can use to model an imbalanced dataset.

Hopefully there
are one or two that you can take off the shelf and apply immediately, for
example changing your accuracy metric and resampling your dataset. Both are
fast and will have an impact straight away.

Which method are you going to try?

A Final
Word, Start Small

Remember that we
cannot know which approach is going to best serve you and the dataset you are
working on.

You can use some
expert heuristics to pick this method or that, but in the end, the best advice
I can give you is to “become the scientist” and empirically test each method
and select the one that gives you the best results.

Start small and
build upon what you learn.

Want More? Further Reading…

There are
resources on class imbalance if you know where to look, but they are few and
far between.

I’ve looked and
the following are what I think are the cream of the crop. If you’d like to dive
deeper into some of the academic literature on dealing with class imbalance,
check out some of the links below.

Books

• Imbalanced Learning: Foundations, Algorithms, and
  Applications

Papers

• Data Mining for Imbalanced Datasets: An Overview
• Learning from Imbalanced Data
• Addressing the Curse of Imbalanced Training Sets:
  One-Sided Selection (PDF)
• A Study of
  the Behavior of Several Methods for Balancing Machine Learning Training Data

Did you find
this post useful? Still have questions?

Leave a comment
and let me know about your problem and any questions you still have about
handling imbalanced classes.