Research any topic before you write.

Find related topics. | Discover entities. | See connections. | Build a topical map.

Minimum redundancy feature selection

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…

Overview, Related Topics & Entities

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Research this topic

Explore the main themes, entities and connections around Minimum redundancy feature selection. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Minimum redundancy feature selection

Nodes17
Edges16
Triples24
Avg. degree1.88
Density0.117647
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Minimum redundancy feature selection

Top relations

related to External links · 21
Minimum redundancy feature selection → August, CA, Chris Ding, Conference, CSB, Ding, Feature, Hanchuan Peng, IEEE Computer Society Bioinformatics, IEEE Transactions, Long, Machine Intelligence, Microarray Gene Expression Data, No, Pages, Pattern Analysis, Peng, Penglab, Stanford, USA
is a · 1
Minimum redundancy feature selection → 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…

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

mrmr selection used relevance features redundancy classification feature relevant maximum many selected variable dependency minimum mutual information pattern machine peng

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Minimum redundancy feature selectionis aalgorithm 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…0.90text
cancer diagnosisinstance ofThese 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 applica…0.80text
speech recognition.Features can be selected in many different waysinstance ofThese 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 applica…0.80text
Minimum redundancy feature selectionrelated to External linksPeng0.60section
Minimum redundancy feature selectionrelated to External linksLong0.60section
Minimum redundancy feature selectionrelated to External linksDing0.60section
Minimum redundancy feature selectionrelated to External linksFeature0.60section
Minimum redundancy feature selectionrelated to External linksIEEE Transactions0.60section
Minimum redundancy feature selectionrelated to External linksPattern Analysis0.60section
Minimum redundancy feature selectionrelated to External linksMachine Intelligence0.60section
Minimum redundancy feature selectionrelated to External linksVol0.60section
Minimum redundancy feature selectionrelated to External linksNo0.60section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
    For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.