International Journal of Operations & Production Management, 2000
ABSTRACT In treating both sewage and storm runoff, wastewater treatment plants are important to m... more ABSTRACT In treating both sewage and storm runoff, wastewater treatment plants are important to maintaining a healthy environment. If the plant operations managers do not respond correctly to plant conditions, environmental damage resulting in the deterioration of human health may be the result. Unfortunately, there are no formal models to help these managers; they rely upon their own intuition to manage the plants. The purpose of this paper is to investigate the effectiveness of various models, originally used for manufacturing, to detect process conditions in wastewater treatment facilities. We compare and contrast the performance of five statistical models and three neural network architectures. The data used in the research is 527 daily measurements of 38 sensor readings of the process state variables of an urban wastewater treatment plant.
International Conference on Artificial Intelligence, 2005
Considerable research effort has been expended to identify more accurate models for decision supp... more Considerable research effort has been expended to identify more accurate models for decision support systems in financial decision domains including bankruptcy prediction. The focus of this earlier work has been to identify the “single best” prediction model from a collection that includes simple parametric models, nonparametric models that directly estimate data densities, and nonlinear pattern recognition models such as neural networks. Recent theories suggest this work may be misguided in that ensembles of predictors provide more accurate generalization than the reliance on a single model. This paper investigates the role of model diversity in the accuracy of ensemble solutions for bankruptcy detection. We employ twenty two diverse models as base classifiers. The generalization ability of the traditional bagging ensemble is found to be significantly improved by increasing model diversity in the ensemble.
The model selection strategy is an important determinant of the performance and acceptance of a m... more The model selection strategy is an important determinant of the performance and acceptance of a medical diagnostic decision support system based on supervised learning algorithms. This research investigates the potential of various selection strategies from a population of 24 classification models to form ensembles in order to increase the accuracy of decision support systems for the early detection and diagnosis
This research investigates the potential for two forms of error diversity (ability diversity and ... more This research investigates the potential for two forms of error diversity (ability diversity and diversity of cognitive style) to increase the accuracy of multi-agent group decision processes. An experimental methodology is employed that rigorously controls for the sources of error ...
... With the widespread use of electronic data capture and automation of medical records, medical... more ... With the widespread use of electronic data capture and automation of medical records, medical diagnostic decision ... A relatively small hidden layer results in a model with a higher error bias and a ... In this research, we use five different hidden layer designs from small to large for ...
... With the widespread use of electronic data capture and automation of medical records, medical... more ... With the widespread use of electronic data capture and automation of medical records, medical diagnostic decision ... A relatively small hidden layer results in a model with a higher error bias and a ... In this research, we use five different hidden layer designs from small to large for ...
International Journal of Operations & Production Management
In treating both sewage and storm runoff, wastewater treatment plants are important to maintainin... more In treating both sewage and storm runoff, wastewater treatment plants are important to maintaining a healthy environment. If the plant operations managers do not respond correctly to plant conditions, environmental damage resulting in the deterioration of human health may be the result. Unfortunately, there are no formal models to help these managers; they rely upon their own intuition to manage the plants. The purpose of this paper is to investigate the effectiveness of various models, originally used for manufacturing, to detect process conditions in wastewater treatment facilities. We compare and contrast the performance of five statistical models and three neural network architectures. The data used in the research is 527 daily measurements of 38 sensor readings of the process state variables of an urban wastewater treatment plant.
International Journal of Operations & Production Management, 2000
ABSTRACT In treating both sewage and storm runoff, wastewater treatment plants are important to m... more ABSTRACT In treating both sewage and storm runoff, wastewater treatment plants are important to maintaining a healthy environment. If the plant operations managers do not respond correctly to plant conditions, environmental damage resulting in the deterioration of human health may be the result. Unfortunately, there are no formal models to help these managers; they rely upon their own intuition to manage the plants. The purpose of this paper is to investigate the effectiveness of various models, originally used for manufacturing, to detect process conditions in wastewater treatment facilities. We compare and contrast the performance of five statistical models and three neural network architectures. The data used in the research is 527 daily measurements of 38 sensor readings of the process state variables of an urban wastewater treatment plant.
International Journal of Operations & Production Management, 2000
ABSTRACT In treating both sewage and storm runoff, wastewater treatment plants are important to m... more ABSTRACT In treating both sewage and storm runoff, wastewater treatment plants are important to maintaining a healthy environment. If the plant operations managers do not respond correctly to plant conditions, environmental damage resulting in the deterioration of human health may be the result. Unfortunately, there are no formal models to help these managers; they rely upon their own intuition to manage the plants. The purpose of this paper is to investigate the effectiveness of various models, originally used for manufacturing, to detect process conditions in wastewater treatment facilities. We compare and contrast the performance of five statistical models and three neural network architectures. The data used in the research is 527 daily measurements of 38 sensor readings of the process state variables of an urban wastewater treatment plant.
International Conference on Artificial Intelligence, 2005
Considerable research effort has been expended to identify more accurate models for decision supp... more Considerable research effort has been expended to identify more accurate models for decision support systems in financial decision domains including bankruptcy prediction. The focus of this earlier work has been to identify the “single best” prediction model from a collection that includes simple parametric models, nonparametric models that directly estimate data densities, and nonlinear pattern recognition models such as neural networks. Recent theories suggest this work may be misguided in that ensembles of predictors provide more accurate generalization than the reliance on a single model. This paper investigates the role of model diversity in the accuracy of ensemble solutions for bankruptcy detection. We employ twenty two diverse models as base classifiers. The generalization ability of the traditional bagging ensemble is found to be significantly improved by increasing model diversity in the ensemble.
The model selection strategy is an important determinant of the performance and acceptance of a m... more The model selection strategy is an important determinant of the performance and acceptance of a medical diagnostic decision support system based on supervised learning algorithms. This research investigates the potential of various selection strategies from a population of 24 classification models to form ensembles in order to increase the accuracy of decision support systems for the early detection and diagnosis
This research investigates the potential for two forms of error diversity (ability diversity and ... more This research investigates the potential for two forms of error diversity (ability diversity and diversity of cognitive style) to increase the accuracy of multi-agent group decision processes. An experimental methodology is employed that rigorously controls for the sources of error ...
... With the widespread use of electronic data capture and automation of medical records, medical... more ... With the widespread use of electronic data capture and automation of medical records, medical diagnostic decision ... A relatively small hidden layer results in a model with a higher error bias and a ... In this research, we use five different hidden layer designs from small to large for ...
... With the widespread use of electronic data capture and automation of medical records, medical... more ... With the widespread use of electronic data capture and automation of medical records, medical diagnostic decision ... A relatively small hidden layer results in a model with a higher error bias and a ... In this research, we use five different hidden layer designs from small to large for ...
International Journal of Operations & Production Management
In treating both sewage and storm runoff, wastewater treatment plants are important to maintainin... more In treating both sewage and storm runoff, wastewater treatment plants are important to maintaining a healthy environment. If the plant operations managers do not respond correctly to plant conditions, environmental damage resulting in the deterioration of human health may be the result. Unfortunately, there are no formal models to help these managers; they rely upon their own intuition to manage the plants. The purpose of this paper is to investigate the effectiveness of various models, originally used for manufacturing, to detect process conditions in wastewater treatment facilities. We compare and contrast the performance of five statistical models and three neural network architectures. The data used in the research is 527 daily measurements of 38 sensor readings of the process state variables of an urban wastewater treatment plant.
International Journal of Operations & Production Management, 2000
ABSTRACT In treating both sewage and storm runoff, wastewater treatment plants are important to m... more ABSTRACT In treating both sewage and storm runoff, wastewater treatment plants are important to maintaining a healthy environment. If the plant operations managers do not respond correctly to plant conditions, environmental damage resulting in the deterioration of human health may be the result. Unfortunately, there are no formal models to help these managers; they rely upon their own intuition to manage the plants. The purpose of this paper is to investigate the effectiveness of various models, originally used for manufacturing, to detect process conditions in wastewater treatment facilities. We compare and contrast the performance of five statistical models and three neural network architectures. The data used in the research is 527 daily measurements of 38 sensor readings of the process state variables of an urban wastewater treatment plant.
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