Evolving Decision Rules to Predict Investment Opportunities

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Garcia:2008,

• author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
• title = "Evolving Decision Rules to Predict Investment Opportunities",
• journal = "International Journal of Automation and Computing",
• year = "2008",
• volume = "5",
• number = "1",
• pages = "22--31",
• month = jan,
• keywords = "genetic algorithms, genetic programming, Machine learning, classification, imbalanced classes, evolution of rules",
• publisher = "Institute of Automation, Chinese Academy of Sciences, co-published with Springer-Verlag GmbH",
• ISSN = "1476-8186",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.2149",
• DOI = "doi:10.1007/s11633-008-0022-2",
• size = "10 pages",
• abstract = "This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules (EDR) to detect financial opportunities. The objective of EDR is to classify the minority class (positive cases) in imbalanced environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are: 1) to show that EDR produces a range of solutions to suit the investor's preferences and 2) to analyse the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive cases in imbalanced data sets. Experimental results show that: 1) EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2) a bigger collection of rules is able to classify more positive cases in imbalanced environments.",
• affiliation = "University of Essex Department of Computer Science Wivenhoe Park Colchester CO4 3SQ UK",

}

Genetic Programming entries for Alma Lilia Garcia Almanza Edward P K Tsang

Citations