Related references
Note: Only part of the references are listed.Normalized Mutual Information Feature Selection
Pablo. A. Estevez et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2009)
A filter model for feature subset selection based on genetic algorithm
M. E. Elalami
KNOWLEDGE-BASED SYSTEMS (2009)
Class dependent feature scaling method using naive Bayes classifier for text datamining
Eunseog Youn et al.
PATTERN RECOGNITION LETTERS (2009)
Monte Carlo feature selection for supervised classification
Michal Draminski et al.
BIOINFORMATICS (2008)
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Jinjie Huang et al.
PATTERN RECOGNITION LETTERS (2007)
A blocking strategy to improve gene selection for classification of gene expression data
Gianluca Bontempi
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2007)
Stochastic local search for the FEATURE SET problem, with applications to microarray data
Andreas A. Albrecht
APPLIED MATHEMATICS AND COMPUTATION (2006)
Gene subset selection in kernel-induced feature space
Satoshi Niijima et al.
PATTERN RECOGNITION LETTERS (2006)
Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis
Shinn-Ying Ho et al.
BIOSYSTEMS (2006)
Significance of gene ranking for classification of microarray samples
Chaolin Zhang et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2006)
Information gain and divergence-based feature selection for machine learning-based text categorization
CK Lee et al.
INFORMATION PROCESSING & MANAGEMENT (2006)
Analysis of recursive gene selection approaches from microarray data
F Li et al.
BIOINFORMATICS (2005)
Feature selection algorithms to find strong genes
PJS Silva et al.
PATTERN RECOGNITION LETTERS (2005)
Multiple SVM-RFE for gene selection in cancer classification with expression data
KB Duan et al.
IEEE TRANSACTIONS ON NANOBIOSCIENCE (2005)
Acute myeloid leukemias with reciprocal rearrangements can be distinguished by specific gene expression profiles
C Schoch et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2002)
Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning
MA Shipp et al.
NATURE MEDICINE (2002)
Gene selection for cancer classification using support vector machines
I Guyon et al.
MACHINE LEARNING (2002)
Gene expression correlates of clinical prostate cancer behavior
D Singh et al.
CANCER CELL (2002)
Share Paper
