Feature selection

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Subset selection

Search algorithm Hill climbing Metrics Targeted projection pursuit Best first

13 related topics

Optimality criteria

Common criteria Akaike information criterion Mallows's Cp Information theory Maximum entropy principle

10 related topics

Hilbert-Schmidt Independence Criterion Lasso based feature selection

Trace Gram matrices Centering matrix Identity matrix If and only if

7 related topics

Information theory-based feature selection mechanisms

Indicator function Optimization problem Parity function Quadratic programming Joint mutual information

6 related topics

Topics to explore

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Overview

Features Feature (machine learning)
Curse of dimensionality
Symmetries Symmetric space
Feature extraction
Overfitting

Introduction

Selection algorithm
Mutual information
Pointwise mutual information
Pearson product-moment correlation coefficient
Relief-based algorithms Relief (feature selection)
Significance tests Statistical hypothesis testing
Cross-validation Cross-validation (statistics)
Support Vector Machines
LASSO Lasso (statistics)
Elastic net regularization
Ridge regression
Regression analysis
Stepwise regression
Greedy algorithm
Branch and bound

Subset selection

Optimality criteria

Structure learning

Structure learning Structured prediction
Bayesian Network
Directed Directed graph
Graphical model
Markov blanket

Information theory-based feature selection mechanisms

Indicator function
Optimization problem
Parity function
Quadratic programming
Joint mutual information Joint mutual information?action=edit&redlink=1
Conditional mutual information

Hilbert-Schmidt Independence Criterion Lasso based feature selection

Correlation feature selection

Spearman's ρ Spearman's rank correlation coefficient
Symmetrical uncertainty Mutual Information
Linear programming
Branch-and-bound algorithms Branch-and-bound algorithm

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Map overview Semantic statistics

Nodes90

Edges89

Triples126

Avg. degree1.98

Density0.022222

Components1

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Feature selection

Top relations

related to Further reading · 30

Feature selection → Algorithms, An Introduction, Andre, Bibcode, Classification, Clustering, Data Engineering, Data Mining, Elisseeff, Guyon, Harrell, Hiroshi, Huan, IEEE Transactions, Isabelle, ISBN, Journal, Knowledge, Knowledge Discovery, Lei

related to Introduction · 27

Feature selection → AEFS, Any, As, Bolasso, Common, Counting, Each, Elastic, Embedded, FeaLect, Filter, Filters, However, Improvements, L1, L2, LASSO, Many, One, Pearson

related to Optimality criteria · 13

Feature selection → AIC, Akaike, Bayesian, BIC, Bonferroni, Examples, FDR, Mallows's Cp, Many, MDL, Other, RIC, The

related to Feature selection embedded in learning algorithms · 12

Feature selection → Auto-encoding, Compared, It, LASSO, Numeric, Recommender, RMNL, RRF, Some, SVMRegularized, The, These

related to External links · 10

Feature selection → Arizona State University, Feature Selection Package, MATLAB, Matlab Code, Minimum-redundancy-maximum-relevance, Naive Bayes, NIPS, Open, Visual Basic Archived, Wayback Machine

related to Structure learning · 6

Feature selection → Bayesian Network, Feature, Filter, Markov, Markov Blanket, The

related to Correlation feature selection · 5

Feature selection → CFS, Good, Here, The, The CFS

related to Information theory-based feature selection mechanisms · 5

Feature selection → Calculate, Repeat, Select, There, They

related to Quadratic programming feature selection · 4

Feature selection → It, QFPS, QPFS, While

related to Joint mutual information · 3

Feature selection → Brown, In, The

Important terminology Word statistics

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Important terminology

feature features selection model methods subset algorithm information data filter displaystyle mutual set variables learning lasso score used problem selected

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
Feature selection	is a	process of selecting a subset of relevant features	0.90	text
Feature selection	is a	specific case of a more general paradigm called structure learning	0.90	text
the Gaussian kernel is used.The HSIC Lasso can be written as H S I C L a s s o	instance of	and is zero if and only if two random variables are statistically independent when a universal reproducing kernel	0.80	text
the dual augmented Lagrangian method	instance of	and thus it can be efficiently solved with a state-of-the-art Lasso solver	0.80	text
normalization	instance of	require little data preprocessing	0.80	text
Feature selection	related to Application of feature selection metaheuristics	This	0.60	section
Feature selection	related to Application of feature selection metaheuristics	Hammon	0.60	section
Feature selection	related to Correlation feature selection	The	0.60	section
Feature selection	related to Correlation feature selection	CFS	0.60	section
Feature selection	related to Correlation feature selection	Good	0.60	section
Feature selection	related to Correlation feature selection	Here	0.60	section
Feature selection	related to Correlation feature selection	The CFS	0.60	section

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Subset selection

Optimality criteria

Hilbert-Schmidt Independence Criterion Lasso based feature selection

Information theory-based feature selection mechanisms

Key facts & relationships

Topics to explore

Overview

Introduction

Subset selection

Optimality criteria

Structure learning

Information theory-based feature selection mechanisms

Hilbert-Schmidt Independence Criterion Lasso based feature selection

Correlation feature selection

Regularized trees

Overview on metaheuristics methods

Feature selection embedded in learning algorithms

Advanced semantic analysis

Map overview Semantic statistics

Feature selection

How this topic connects Entity context

Feature selection

Top relations

Important terminology Word statistics

Important terminology

Entity relationships Subject–Predicate–Object triples

Related concept clusters Concept neighborhoods

Connections between topic areas Semantic bridges