mathematics

Synthetic minority oversampling technique

Synthetic minority oversampling technique is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Synthetic minority oversampling technique rather than just read about it. In short: In statistics, synthetic minority oversampling technique (SMOTE) is a method for oversampling samples when dealing with imbalanced classification categories within a dataset. The problem with doing statistical inference and modelling on imbalanced datasets is that the inferences and results from those analyses will be biased towards the majority class.

Key takeaways

Synthetic minority oversampling technique belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Synthetic minority oversampling technique to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Synthetic minority oversampling technique from memory before moving on to harder problems.

Reference excerpt

In statistics, synthetic minority oversampling technique (SMOTE) is a method for oversampling samples when dealing with imbalanced classification categories within a dataset. The problem with doing statistical inference and modelling on imbalanced datasets is that the inferences and results from those analyses will be biased towards the majority class. Other solutions undersample the majority class to be equivalently represented in the data with the minority class. Instead of undersampling the majority class, SMOTE oversamples the minority class. However, this technique has been shown to yield poorly calibrated models, with an overestimated probability to belong to the minority class.

Limitations SMOTE does come with some limitations and challenges:

Overfitting during the training process Favorable outcomes in the machine learning process, but questionable translation to practical uses Synthetically created sample may belong to a different class Synthetic data may not match the original distribution of the minority class

Variations Two variations to the SMOTE algorithm were proposed in the initial SMOTE paper:

SMOTE-NC: applies to datasets with a mix of nominal and continuous data SMOTE-N: accounts for nominal features, with the nearest neighbors algorithm being computed using the modified version of Value Difference Metric (VDM), which looks at the overlap of feature values over all feature vectors Other variations include:

ADASYN: use a weighted distribution for different minority class examples according to their level of difficulty in learning Borderline-SMOTE: only the minority examples near the borderline are over-sampled SMOTE-Tomek: applying Tomek links to the oversampled training set as a data cleaning step to remove samples overlapping the category boundaries SMOTE-ENN: uses the Edited Nearest Neighbor Rule, which removes any example whose class label differs from the class of at least two of its three nearest neighbors

Algorithm The SMOTE algorithm can be abstracted with the following pseudocode:

where

N is the amount of SMOTE, where the amount of SMOTE is assumed to be a multiple of one hundred T is the number of minority class samples k is the number of nearest neighbors Populate() is the generating function for new synthetic minority samples If N is less than 100%, the minority class samples will be randomized, as only a random subset of them will have SMOTE applied to them.

Implementations Since the introduction of the SMOTE method, there have been a number of software implementations:

imbalanced-learn smote_variants: implementation of 86 SMOTE variations ImbalancedLearningRegression

See also Stratified sampling Oversampling and undersampling in data analysis

References

Worked examples

Example 1 — a first encounter with Synthetic minority oversampling technique

Start with the simplest possible case. Write down what Synthetic minority oversampling technique claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Synthetic minority oversampling technique before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Synthetic minority oversampling technique ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Synthetic minority oversampling technique

In research: Synthetic minority oversampling technique appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Synthetic minority oversampling technique in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Synthetic minority oversampling technique is common in secondary-school and first-year university syllabi. It links to neighbouring topics Sampling (statistics), so understanding it makes those chapters shorter.
In everyday life: Look for Synthetic minority oversampling technique outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Synthetic minority oversampling technique in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Synthetic minority oversampling technique means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Synthetic minority oversampling technique out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Synthetic minority oversampling technique in simple terms?

Why does Synthetic minority oversampling technique matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Synthetic minority oversampling technique?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Synthetic minority oversampling technique.