The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Algorithmic Implementation of Stochastic Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Ensembling neural networks: many could be better than all
Artificial Intelligence
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Self and Nonself Revisited: Lessons from Modelling the Immune Network
Proceedings of the Third European Conference on Advances in Artificial Life
Online Ensemble Learning: An Empirical Study
Machine Learning
Comparing Pure Parallel Ensemble Creation Techniques Against Bagging
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
Meta-learning orthographic and contextual models for language independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
Selective SVMs ensemble driven by immune clonal algorithm
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Designing ensembles of fuzzy classification systems: an immune-inspired approach
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
An antibody network inspired evolutionary framework for distributed object computing
Information Sciences: an International Journal
Evolving an Ensemble of Neural Networks Using Artificial Immune Systems
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Expert Systems with Applications: An International Journal
An immune inspired co-evolutionary affinity network for prefetching of distributed object
Journal of Parallel and Distributed Computing
Design of vehicle speed controller based on immune feed-back
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Dual-population based coevolutionary algorithm for designing RBFNN with feature selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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This paper presents a new method for constructing ensembles of classifiers based on immune network theory, one of the most interesting paradigms within the field of artificial immune systems. Ensembles of classifiers are a very interesting alternative to single classifiers when facing difficult problems. In general, ensembles are able to achieve better performance in terms of learning and generalisation error. Artificial immune system is a new paradigm within the field of bioinspired algorithms that mimics the behaviour of the natural immune system of animals to develop solutions for a given problem. Within artificial immune systems, one of the most innovative and appealing fields is immune network theory. We construct an immune network that constitutes an ensemble of classifiers. Using a neural network as base classifier we have compared the performance of this ensemble with five standard methods of ensemble construction. This comparison is made using 35 real-world classification problems from the UCI Machine Learning Repository. The results show that the proposed model exhibits a general advantage over the standard methods.