Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals.  develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP),  propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases.  have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as ll as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients.
['Kwong-Sak Leung', 'Man Leung Wong', 'Wai Lam', 'Zhenyuan Wang', 'Kebin Xu']
I am writing a research paper on a idea Learning nonlinear multiregression networks based on evolutionary computation
[{"from": "human", "value": "I am writing a research paper on a idea Learning nonlinear multiregression networks based on evolutionary computation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals.  develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP),  propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases.  have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as ll as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients."}]