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A generic scheme for generating prediction rules using rough sets. (English) Zbl 1160.91398

Abraham, Ajith (ed.) et al., Rough set theory: A true landmark in data analysis. Berlin: Springer (ISBN 978-3-540-89920-4/hbk; 978-3-540-89921-1/ebook). Studies in Computational Intelligence 174, 163-186 (2009).
Summary: This chapter presents a generic scheme for generating prediction rules based on rough set approach for stock market prediction. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data, which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. For comparison, the results obtained using rough set approach were compared to that of artificial neural networks and decision trees. Empirical results illustrate that rough set approach achieves a higher overall prediction accuracy reaching over 97% and generates more compact and fewer rules than neural networks and decision tree algorithm.
For the entire collection see [Zbl 1157.68001].

MSC:

91B84 Economic time series analysis
68T37 Reasoning under uncertainty in the context of artificial intelligence
68T05 Learning and adaptive systems in artificial intelligence
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming

Software:

RSBR_
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References:

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