Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
New Measure of Classifier Dependency in Multiple Classifier Systems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Methods for Dynamic Classifier Selection
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Fuzzy Classifier Design
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation
Applied Soft Computing
Pareto analysis for the selection of classifier ensembles
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Identifying core sets of discriminatory features using particle swarm optimization
Expert Systems with Applications: An International Journal
A genetic encoding approach for learning methods for combining classifiers
Expert Systems with Applications: An International Journal
Mixture of Gaussians Model for Robust Pedestrian Images Detection
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Classifier subset selection for biomedical named entity recognition
Applied Intelligence
Hybrid Repayment Prediction for Debt Portfolio
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Prediction of Sequential Values for Debt Recovery
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications
Neural Processing Letters
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Particle swarm optimisation of multiple classifier systems
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Feature selection for efficient gender classification
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
A genetic-algorithm-based fusion system optimization for 3D image interpretation
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Evolutionary optimization of regression model ensembles in steel-making process
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Expert Systems with Applications: An International Journal
A survey of multiple classifier systems as hybrid systems
Information Fusion
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An intense research around classifier fusion in recent years revealed that combining performance strongly depends on careful selection of classifiers to be combined. Classifier performance depends, in turn, on careful selection of features, which could be further restricted by the subspaces of the data domain. On the other hand, there is already a number of classifier fusion techniques available and the choice of the most suitable method depends back on the selections made within classifier, features and data spaces. In all these multidimensional selection tasks genetic algorithms (GA) appear to be one of the most suitable techniques providing reasonable balance between searching complexity and the performance of the solutions found. In this work, an attempt is made to revise the capability of genetic algorithms to be applied to selection across many dimensions of the classifier fusion process including data, features, classifiers and even classifier combiners. In the first of the discussed models the potential for combined classification improvement by GA-selected weights for the soft combining of classifier outputs has been investigated. The second of the proposed models describes a more general system where the specifically designed GA is applied to selection carried out simultaneously along many dimensions of the classifier fusion process. Both, the weighted soft combiners and the prototype of the three-dimensional fusion-classifier-feature selection model have been developed and tested using typical benchmark datasets and some comparative experimental results are also presented.