Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Knowledge Acquisition form Examples Vis Multiple Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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This paper presents a combination of Combined Multiple Models (CMM) technique and evolutionary approach that is used for tuning of multiple parameters. Proposed hybrid classifier was tested in microarray gene expression analysis domain. This domain was chosen intentionally, because of the nature of Combined Multiple Models classifiers that are specialized in solving problems with high dimensionality and contain low number of samples. Evolutionary tuning of parameters in combination with validation dataset enables fine tuning of parameters that are usually set to pre-defined values. Using this technique another step in leveling the accuracy of comprehensible classifiers to those represented by ensembles of classifiers was made.