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» Variable selection using neural-network models
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ISIPTA
1999
IEEE
108views Mathematics» more  ISIPTA 1999»
15 years 2 months ago
Coherent Models for Discrete Possibilistic Systems
We consider discrete possibilistic systems for which the available information is given by one-step transition possibilities and initial possibilities. These systems can be repres...
Hugo J. Janssen, Gert De Cooman, Etienne E. Kerre
BMCBI
2010
150views more  BMCBI 2010»
14 years 7 months ago
Kernel based methods for accelerated failure time model with ultra-high dimensional data
Background: Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L1 and Lp penalty have been extensively studied in survi...
Zhenqiu Liu, Dechang Chen, Ming Tan, Feng Jiang, R...
GECCO
2008
Springer
186views Optimization» more  GECCO 2008»
14 years 11 months ago
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...
A. K. M. Khaled Ahsan Talukder, Michael Kirley, Ra...
AUSDM
2007
Springer
173views Data Mining» more  AUSDM 2007»
15 years 4 months ago
The Use of Various Data Mining and Feature Selection Methods in the Analysis of a Population Survey Dataset
This paper reports the results of feature reduction in the analysis of a population based dataset for which there were no specific target variables. All attributes were assessed a...
Ellen Pitt, Richi Nayak
WSC
2004
14 years 11 months ago
Simulated Annealing for Selection of Experimental Regions in Response Surface Methodology Applications
In this paper we describe a methodology that includes the complementary use of simulated annealing and response surface methodology (RSM). The methodology was developed for analys...
Jeffrey B. Schamburg, Donald E. Brown