Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Fuzzy Sets and Systems - Special issue: fuzzy sets and management science
Optimal linear combinations of neural networks
Neural Networks
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Classification Based on Iterative Object Symmetry Transform
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
A Clustering Performance Measure Based on Fuzzy Set Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Constructing accurate fuzzy classifiers: A new adaptive method for rule-weight specification
International Journal of Knowledge-based and Intelligent Engineering Systems
| Hi-index | 0.00 |
This paper introduces a genetic algorithm able to combine different classifiers based on different distance functions. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of a vote strategy. The method has been applied to the classification of four types of biological cells, results show an improvement of the recognition rate using the genetic algorithm combination strategy compared with the recognition rate of each single classifier.