computer science

Minimum redundancy feature selection

Minimum redundancy feature selection is a computer science 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 Minimum redundancy feature selection rather than just read about it. In short: Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and narrow down their relevance and is usually described in its pairing with relevant feature selection as Minimum Redundancy Maximum Relevance (mRMR). This method was first proposed in 2003 by Hanchuan Peng and Chris Ding, followed by a theoretical formulation based on mutu…

Key takeaways

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

Reference excerpt

Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and narrow down their relevance and is usually described in its pairing with relevant feature selection as Minimum Redundancy Maximum Relevance (mRMR). This method was first proposed in 2003 by Hanchuan Peng and Chris Ding, followed by a theoretical formulation based on mutual information, along with the first definition of multivariate mutual information, published in IEEE Trans. Pattern Analysis and Machine Intelligence in 2005. Feature selection, one of the basic problems in pattern recognition and machine learning, identifies subsets of data that are relevant to the parameters used and is normally called Maximum Relevance. These subsets often contain material which is relevant but redundant and mRMR attempts to address this problem by removing those redundant subsets. mRMR has a variety of applications in many areas such as cancer diagnosis and speech recognition. Features can be selected in many different ways. One scheme is to select features that correlate strongest to the classification variable. This has been called maximum-relevance selection. Many heuristic algorithms can be used, such as the sequential forward, backward, or floating selections. On the other hand, features can be selected to be mutually far away from each other while still having "high" correlation to the classification variable. This scheme, termed as Minimum Redundancy Maximum Relevance (mRMR) selection has been found to be more powerful than the maximum relevance selection. As a special case, the "correlation" can be replaced by the statistical dependency between variables. Mutual information can be used to quantify the dependency. In this case, it is shown that mRMR is an approximation to maximizing the dependency between the joint distribution of the selected features and the classification variable. Studies have tried different measures for redundancy and relevance measures. A recent study compared several measures within the context of biomedical images.

References

External links Peng, H.C., Long, F., and Ding, C., "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp. 1226–1238, 2005. Chris Ding and Hanchuan Peng, "Minimum Redundancy Feature Selection from Microarray Gene Expression Data". 2nd IEEE Computer Society Bioinformatics Conference (CSB 2003), 11–14 August 2003, Stanford, CA, USA. Pages 523–529. Penglab mRMR

Worked examples

Example 1 — a first encounter with Minimum redundancy feature selection

Start with the simplest possible case. Write down what Minimum redundancy feature selection claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, 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 Minimum redundancy feature selection 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 Minimum redundancy feature selection 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 Minimum redundancy feature selection

In research: Minimum redundancy feature selection appears in computer science 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 Minimum redundancy feature selection 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: Minimum redundancy feature selection is common in secondary-school and first-year university syllabi. It links to neighbouring topics Machine learning algorithms, so understanding it makes those chapters shorter.
In everyday life: Look for Minimum redundancy feature selection 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 Minimum redundancy feature selection in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Minimum redundancy feature selection 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 Minimum redundancy feature selection out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Minimum redundancy feature selection in simple terms?

Why does Minimum redundancy feature selection matter?

Because it connects several computer science 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 Minimum redundancy feature selection?

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 Minimum redundancy feature selection.