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Everything about Random subspace method
Method in machine learning
In machine learning the random subspace method, also called attribute bagging or feature bagging, is an ensemble learning method that attempts to reduce the correlation between estimators in an ensemble by training them on random samples of features instead of the entire feature set. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Random subspace methodA 5-minute orientation
- Skim a recent paper: Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault DiagnosisInfluential work since 2022
- Pick up a classic book: Computer Engineering and TechnologyThe most-reprinted book on the subject
- Read the landmark paper: The random subspace method for constructing decision forestsCited 5,555 times
- Go sideways: Random forestA closely related topic
- Go sideways: Ensemble learningA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
The random subspace method for constructing decision forests
Bagging, Boosting and the Random Subspace Method for Linear Classifiers
Random subspace method for multivariate feature selection
POS-RS: A Random Subspace method for sentiment classification based on part-of-speech analysis
A new random subspace method incorporating sentiment and textual information for financial distress prediction
Semi-random subspace method for face recognition
Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method
Boosting random subspace method
🆕 Influential since 2022
Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis
Modal Parameter Identification of the Improved Random Decrement Technique-Stochastic Subspace Identification Method Under Non-Stationary Excitation
Bridge facility health detection method based on DATA random subspace
Q-fully quadratic modeling and its application in a random subspace derivative-free method
A new approach based on integration of random subspace and C4.5 decision tree learning method for spatial prediction of shallow landslides
📚 Books worth reading
Computer Engineering and TechnologyWeixia Xu · 2013
ClusteringRui Xu · 2008
Computer Vision - ACCV 2012 WorkshopsJong-Il Park · 2013
Social Computing, Behavioral-Cultural Modeling and PredictionAriel M. Greenberg · 2013
Intelligent Science and Intelligent Data EngineeringJian Yang · 2013
Coding the MatrixPhilip N. Klein · 2013
Linear Algebra and Learning from DataGilbert Strang · 2019- 📕Problems of computational mathematics and mathematical modellingG. I. Marchuk, V. P. Dymnikov · 1985
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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