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SGAI
2004
Springer
13 years 10 months ago
Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets
In Wrapper based feature selection, the more states that are visited during the search phase of the algorithm the greater the likelihood of finding a feature subset that has a high...
John Loughrey, Padraig Cunningham
CORR
2006
Springer
130views Education» more  CORR 2006»
13 years 5 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...
GECCO
1999
Springer
133views Optimization» more  GECCO 1999»
13 years 9 months ago
Non-Standard Crossover for a Standard Representation - Commonality-Based Feature Subset Selection
The Commonality-Based Crossover Framework has been presented as a general model for designing problem specific operators. Following this model, the Common Features/Random Sample ...
Stephen Y. Chen, Cesar Guerra-Salcedo, Stephen F. ...
GECCO
2003
Springer
13 years 10 months ago
A Kernighan-Lin Local Improvement Heuristic That Solves Some Hard Problems in Genetic Algorithms
We present a Kernighan-Lin style local improvement heuristic for genetic algorithms. We analyze the run-time cost of the heuristic. We demonstrate through experiments that the heur...
William A. Greene
WSC
2007
13 years 7 months ago
Subset selection and optimization for selecting binomial systems applied to supersaturated design generation
The problem of finding the binomial population with the highest success probability is considered when the number of binomial populations is large. A new rigorous indifference zo...
Ning Zheng, Theodore Allen