Efficient All Relevant Feature Selection with Random Ferns

No abstract is available for this paper.

• Publication year

  2017

• Venue

  International Syposium on Methodologies for Intelligent Systems

• Publication date

  2017-06-26

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/978-3-319-60438-1_30
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Robustness of Random Forest-based gene selection methods

  2013cited by this paper
• rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning

  2012cited by this paper
• Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection

  2012cited by this paper
• Forward-time simulation of realistic samples for genome-wide association studies

  2010cited by this paper
• Fast Keypoint Recognition Using Random Ferns

  2010cited by this paper
• Prediction of protein structural classes for low-homology sequences based on predicted secondary structure

  2010cited by this paper
• Detection of gene pathways with predictive power for breast cancer prognosis

  2010cited by this paper
• Boruta – A System for Feature Selection

  2010cited by this paper
• Feature Selection with the Boruta Package

  2010cited by this paper
• Action recognition using Randomised Ferns

  2009cited by this paper
• Exploiting tree-based variable importances to selectively identify relevant variables

  2008cited by this paper
• A review of feature selection techniques in bioinformatics

  2007cited by this paper
• Fast Keypoint Recognition in Ten Lines of Code

  2007cited by this paper
• Image Classification using Random Forests and Ferns

  2007cited by this paper
• Consistent Feature Selection for Pattern Recognition in Polynomial Time

  2007cited by this paper
• Feature Selection Using Ensemble Based Ranking Against Artificial Contrasts

  2006cited by this paper
• Result Analysis of the NIPS 2003 Feature Selection Challenge

  2004cited by this paper
• Gene expression correlates of clinical prostate cancer behavior.

  2002cited by this paper
• Random Forests

  2001cited by this paper
• THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS

  1936cited by this paper

• Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

  2022cites this paper
• Brain connectivity alterations during sleep by closed-loop transcranial neurostimulation predict metamemory sensitivity

  2021cites this paper
• Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity

  2021cites this paper
• Linking drought indices to impacts to support drought risk assessment in Liaoning province, China

  2020cites this paper
• Linking drought indices to impacts to support drought risk assessment in Liaoning province, China

  2019cites this paper