Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/70745
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Type: Conference paper
Title: An experimental study of multi-objective evolutionary algorithms for balancing interpretability and accuracy in fuzzy rulebase classifiers for financial prediction
Author: Ghandar, A.
Michalewicz, Z.
Citation: Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr), 11-15 April, 2011, Paris: pp.1-6
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
ISBN: 9781424499335
Conference Name: IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) (2011 : Paris)
Statement of
Responsibility: 
Adam Ghandar and Zbigniew Michalewicz
Abstract: This paper examines the advantages of simple models over more complex ones for financial prediction. This premise is examined using a genetic fuzzy framework. The interpretability of fuzzy systems is oftentimes put forward as a unique advantageous feature, sometimes to justify effort associated with using fuzzy classifiers instead of alternatives that can be more readily implemented using existing tools. Here we investigate if model interpretability can provide further benefits by realizing useful properties in computationally intelligent systems for financial modeling. We test an approach for learning momentum based strategies that predict price movements of the Bombay Stock Exchange (BSE). The paper contributes an experimental evaluation of the relationship between the predictive capability and interpretability of fuzzy rule based systems obtained using Multi- Objective Evolutionary Algorithms (MOEA.)
Rights: © Copyright 2011 IEEE
RMID: 0020115394
DOI: 10.1109/CIFER.2011.5953570
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5937317
Appears in Collections:Computer Science publications

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