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Trade-Off Between Diversity and Accuracy in Ensemble Generation
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Outgoing claims: the papers this work cites, builds on, or argues with.
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An Introduction to Boosting and Leveraging
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Using Negative Correlation to Evolve Fault-Tolerant Circuits
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Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems
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Every niching method has its niche: Fitness sharing and implicit sharing compared
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Combining Artificial Neural Nets
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DIVACE: Diverse and Accurate Ensemble Learning Algorithm
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Use of methodological diversity to improve neural network generalisation
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Ensemble Learning Using Multi-Objective Evolutionary Algorithms
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Error-Correcting Output Coding Corrects Bias and Variance
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Diversity creation methods: a survey and categorisation
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Evolving hybrid ensembles of learning machines for better generalisation
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Basic principles of ROC analysis
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Ensemble learning via negative correlation
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Improving generalization of MLPs with multi-objective optimization
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An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
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On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
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Machine Learning, Neural and Statistical Classification
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Decision combination in multiple classifier systems
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Combination of multiple classifiers using local accuracy estimates
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Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves
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Evolutionary ensembles with negative correlation learning
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PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems
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Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization
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Neural network regularization and ensembling using multi-objective evolutionary algorithms
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Generalization error of ensemble estimators
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IEEE Transactions on Evolutionary Computation
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EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION APPROACH TO CONSTRUCTING NEURAL NETWORK ENSEMBLES FOR REGRESSION
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Speeding Up Backpropagation Using Multiobjective Evolutionary Algorithms
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Bias/Variance Decompositions for Likelihood-Based Estimators
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Using Genetic Algorithms to Explore Pattern Recognition in the Immune System
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Neural Networks and the Bias/Variance Dilemma
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Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
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Neural networks for pattern recognition
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Neural Information Processing Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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