Neural networks and the bias/variance dilemma
Neural Computation
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble learning via negative correlation
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving neural networks through augmenting topologies
Evolutionary Computation
Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Combining Decision Trees and Neural Networks for Drug Discovery
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Face and Hand Gesture Recognition Using Hybrid Classifiers
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
An introduction to boosting and leveraging
Advanced lectures on machine learning
Online ensemble learning
Speeding up backpropagation using multiobjective evolutionary algorithms
Neural Computation
The Knowledge Engineering Review
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Ensemble learning for free with evolutionary algorithms?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A local boosting algorithm for solving classification problems
Computational Statistics & Data Analysis
An efficient modified boosting method for solving classification problems
Journal of Computational and Applied Mathematics
Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery
Applied Soft Computing
Evolutionary product-unit neural networks classifiers
Neurocomputing
Using Boosting to prune Double-Bagging ensembles
Computational Statistics & Data Analysis
Evolving an Ensemble of Neural Networks Using Artificial Immune Systems
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Negative correlation in incremental learning
Natural Computing: an international journal
A novel method for constructing ensemble classifiers
Statistics and Computing
Analysis of bagging ensembles of fuzzy models for premises valuation
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers
Information Sciences: an International Journal
Ensemble learning for customers targeting
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Expert Systems with Applications: An International Journal
A fuzzy evolutionary framework for combining ensembles
Applied Soft Computing
A multiobjective evolutionary programming framework for graph-based data mining
Information Sciences: an International Journal
Performance of an ensemble of ordinary, universal, non-stationary and limit Kriging predictors
Structural and Multidisciplinary Optimization
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Ensembles of learning machines have been formally and empirically shown to outperform (generalise better than) single predictors in many cases. Evidence suggests that ensembles generalise better when they constitute members which form a diverse and accurate set. Additionally, there have been a multitude of theories on how one can enforce diversity within a combined predictor setup. We recently attempted to integrate these theories together into a co-evolutionary framework with a view to synthesising new evolutionary ensemble learning algorithms using the fact that multi-objective evolutionary optimisation is a formidable ensemble construction technique. This paper explicates on the intricacies of the proposed framework in addition to presenting detailed empirical results and comparisons with a wide range of algorithms in the machine learning literature. The framework treats diversity and accuracy as evolutionary pressures which are exerted at multiple levels of abstraction and is shown to be effective.