Optimal linear combinations of neural networks
Neural Networks
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble learning via negative correlation
Neural Networks
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Driven by new regulations and animal welfare, the need to develop in silicomodels has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silicomodel for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals.