Abstract
This paper describes the use of genetic algorithms for inducing fuzzy rulebases within the context of decision support systems for financial trading. The genetic algorithm part of the procedure is based on Packard's algorithm for complex data analysis. The fuzzy pre-processing of the data is achieved by using a Single Linkage clustering algorithm in conjunction with an heuristic cluster selection mechanism. We believe that this hybrid approach has advantages over other ‘black-box’ machine learning procedures in that it produces transparent decision models that are easily understood by decision-makers. Further, the induced decision models lend themselves naturally to judgmental revisions by decision-makers.
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Goonatilake, S., Campbell, J.A., Ahmad, N. (1995). Genetic-fuzzy systems for financial decision making. In: Furuhashi, T. (eds) Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms. WWW 1994. Lecture Notes in Computer Science, vol 1011. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60607-6_14
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DOI: https://doi.org/10.1007/3-540-60607-6_14
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