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» An Experimental Study on Feature Subset Selection Methods
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NIPS
2003
15 years 5 months ago
Feature Selection in Clustering Problems
A novel approach to combining clustering and feature selection is presented. It implements a wrapper strategy for feature selection, in the sense that the features are directly se...
Volker Roth, Tilman Lange
IJCNN
2008
IEEE
15 years 10 months ago
Sparse kernel density estimator using orthogonal regression based on D-Optimality experimental design
— A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experi...
Sheng Chen, Xia Hong, Chris J. Harris
BIBE
2007
IEEE
136views Bioinformatics» more  BIBE 2007»
15 years 5 months ago
A Two-Stage Gene Selection Algorithm by Combining ReliefF and mRMR
Abstract—Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes ...
Yi Zhang, Chris H. Q. Ding, Tao Li
HAIS
2009
Springer
15 years 7 months ago
A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection
Abstract. Cooperative Coevolution is a technique in the area of Evolutionary Computation. It has been applied to many combinatorial problems with great success. This contribution p...
Joaquín Derrac, Salvador García, Fra...
BMCBI
2004
181views more  BMCBI 2004»
15 years 3 months ago
Iterative class discovery and feature selection using Minimal Spanning Trees
Background: Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples ar...
Sudhir Varma, Richard Simon