An agent-based approach for predictions based on multi-dimensional complex data

Ma, T., Nakamori, Y., & Huang, W. (2006). An agent-based approach for predictions based on multi-dimensional complex data. Information Sciences 176 (9) 1156-1174. 10.1016/j.ins.2005.07.011.

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Agent-based approach; Membership function; Prediction
Research Programs:	Transitions to New Technologies (TNT)
Bibliographic Reference:	Information Sciences; 176(9):1156-1174 [2006]
Depositing User:	IIASA Import
Date Deposited:	15 Jan 2016 02:19
Last Modified:	27 Aug 2021 17:38
URI:	https://pure.iiasa.ac.at/7880

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