کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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396353 | 666417 | 2006 | 19 صفحه PDF | دانلود رایگان |
This paper presents an agent-based approach to the identification of prediction models for continuous values from multi-dimensional data, both numerical and categorical. A simple description of the approach is: a number of agents are sent to the investigated data space; at the micro-level, each agent tries to build a local linear model with multi-linear regressions by competing with others; then at the macro-level all surviving agents build a global model by introducing membership functions. Three tests were carried out and the performance of the approach was compared with that of a neural network. The results of the three tests show that the agent-based approach can achieve good performance for some data sets. The approach complements rather than competes with other Soft Computing methods.
Journal: Information Sciences - Volume 176, Issue 9, 8 May 2006, Pages 1156–1174