Buchumschlag

Spectral Feature Selection for Data Mining

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framewo...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Zhao, Zheng Alan, Liu, Huan
Format: Online
Sprache:Englisch
Veröffentlicht: Taylor & Francis 2025
Schlagworte:
Feature Selection Algorithms
Feature Selection
Spectral Feature Selection
Multivariate Formulations
data mining
Data Set
machine learning
Fisher Score
dimensionality reduction
Laplacian Matrix
Similarity Matrix
feature extraction
high-dimensional data processing
Redundant Features
Existing Feature Selection
Normalized Laplacian Matrix
Rank Aggregation
F2 F3 F4 F5 F6
Feature Selection Techniques
F1 F2 F3 F4 F5
TIMP Metallopeptidase Inhibitor
Gene Selection
Computer Nodes
microRNA Microarray
LDA
thema EDItEUR::U Computing and Information Technology::UY Computer science
thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
Online-Zugang:ONIX_20250422_9781439862100_21
Tags: Tag hinzufügen
Keine Tags, Fügen Sie das erste Tag hinzu!
Zusammenfassung:Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervise

Ähnliche Einträge