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An agent-based model is a computer simulation driven by the individual decisions of programmed agents. Such models provide a promising alternative to traditional economic modeling in that they can fully capture the diversity of agents and the institutional detail of the underlying an economic system. In this paper, we provide a brief methodological review of the agent-based approach to modeling financial...
Input variables selection plays a critical role in data-driven modelling, especially for complex systems with high dimensionality between the input/output space. In this paper, a new artificial neural network based forward input selection scheme is proposed. The objective of the proposed scheme is to select the smallest number of important variables as model inputs, which will then be used for neural-fuzzy...
An iterative bootstrapping-based data over-sampling strategy is presented in this paper together with an adaptive neural-fuzzy inference system (ANFIS) to deal with a severely imbalanced data modelling problem. As real industrial data are often very large, containing hundreds of process variables and a huge number of data records, the selection of a compact set of input variables becomes critical...
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