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» Methods for Dynamic Classifier Selection
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ICPR
2006
IEEE
14 years 6 months ago
Adaptive Weighting of Local Classifiers by Particle Filter
This paper presents adaptive weighting method for combining local classifiers by particle filter. In recent years, the effectiveness of combination of local classifiers (features)...
Kazuhiro Hotta
DEXAW
1999
IEEE
97views Database» more  DEXAW 1999»
13 years 9 months ago
Mining Several Data Bases with an Ensemble of Classifiers
The results of knowledge discovery in databases could vary depending on the data mining method. There are several ways to select the most appropriate data mining method dynamicall...
Seppo Puuronen, Vagan Y. Terziyan, Alexander Logvi...
ICDAR
2003
IEEE
13 years 10 months ago
Comparison of Genetic Algorithm and Sequential Search Methods for Classifier Subset Selection
Classifier subset selection (CSS) from a large ensemble is an effective way to design multiple classifier systems (MCSs). Given a validation dataset and a selection criterion, the...
Hongwei Hao, Cheng-Lin Liu, Hiroshi Sako
GECCO
2006
Springer
173views Optimization» more  GECCO 2006»
13 years 9 months ago
Pareto-coevolutionary genetic programming classifier
The conversion and extension of the Incremental ParetoCoevolution Archive algorithm (IPCA) into the domain of Genetic Programming classifier evolution is presented. In order to ac...
Michal Lemczyk, Malcolm I. Heywood
AAAI
2004
13 years 7 months ago
Bayesian Network Classifiers Versus k-NN Classifier Using Sequential Feature Selection
The aim of this paper is to compare Bayesian network classifiers to the k-NN classifier based on a subset of features. This subset is established by means of sequential feature se...
Franz Pernkopf