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BMCBI
2010
224views more  BMCBI 2010»
13 years 7 months ago
An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
Susmita Datta, Vasyl Pihur, Somnath Datta
IJCNN
2007
IEEE
14 years 2 months ago
Distance-based Disagreement Classifiers Combination
— We present a methodology to analyze Multiple Classifiers Systems (MCS) performance, using the diversity concept. The goal is to define an alternative approach to the convention...
Cinthia Obladen de Almendra Freitas, João M...
IJON
2008
116views more  IJON 2008»
13 years 7 months ago
Evolutionary ensemble of diverse artificial neural networks using speciation
Recently, many researchers have designed neural network architectures with evolutionary algorithms but most of them have used only the fittest solution of the last generation. To ...
Kyung-Joong Kim, Sung-Bae Cho
ICANN
2003
Springer
14 years 27 days ago
Confidence Estimation Using the Incremental Learning Algorithm, Learn++
Pattern recognition problems span a broad range of applications, where each application has its own tolerance on classification error. The varying levels of risk associated with ma...
Jeffrey Byorick, Robi Polikar
GECCO
2007
Springer
174views Optimization» more  GECCO 2007»
14 years 1 months ago
Heuristic speciation for evolving neural network ensemble
Speciation is an important concept in evolutionary computation. It refers to an enhancements of evolutionary algorithms to generate a set of diverse solutions. The concept is stud...
Shin Ando