The Strength of Weak Learnability
Machine Learning
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive mixtures of local experts
Neural Computation
A model-free ensemble method for class prediction with application to biomedical decision making
Artificial Intelligence in Medicine
Wastewater BOD forecasting model for optimal operation using robust time-delay neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process. Due to the low accuracy and unstable performance of the traditional effluent quality measurements, we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions. Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms. Ensemble extreme learning machine models overcome variations in different trials of simulations for single model. Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance. The proposed method is verified with the data from an industrial wastewater treatment plant, located in Shenyang, China. Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square, neural network partial least square, single extreme learning machine and ensemble extreme learning machine model.