The Repository Method for Chance Discovery in Financial Forecasting
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Garcia:2006a,
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author = "Alma L Garcia-Almanza and Edward P. K. Tsang",
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title = "The Repository Method for Chance Discovery in
Financial Forecasting",
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ISSN = "0302-9743",
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year = "2006",
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editor = "Bogdan Gabrys and Robert J. Howlett and
Lakhmi C. Jain",
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series = "Lecture Notes in Computer Science",
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volume = "4253",
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booktitle = "KES 2006, Proceedings of the 10th International
Conference on Knowledge-Based Intelligent Information
and Engineering Systems",
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pages = "30--37",
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address = "Bournemouth, UK",
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month = oct # " 9-11",
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publisher = "Springer-Verlag",
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note = "Part III",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-46542-1",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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DOI = "doi:10.1007/11893011_5",
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abstract = "The aim of this work is to forecast future
opportunities in financial stock markets, in
particular, we focus our attention on situations where
positive instances are rare, which falls into the
domain of Chance Discovery. Machine learning
classifiers extend the past experiences into the
future. However the imbalance between positive and
negative cases poses a serious challenge to machine
learning techniques. Because it favours negative
classifications, which has a high chance of being
correct due to the nature of the data. Genetic
Algorithms have the ability to create multiple
solutions for a single problem. To exploit this feature
we propose to analyse the decision trees created by
Genetic Programming. The objective is to extract and
collect different rules that classify the positive
cases. It lets model the rare instances in different
ways, positive cases. It lets model the rare instances
in different ways, increasing the possibility of
identifying similar cases in the future. To illustrate
our approach, it was applied to predict investment
opportunities with very high returns. From experiment
results we showed that the Repository Method can
consistently improve both the recall and the
precision.",
- }
Genetic Programming entries for
Alma Lilia Garcia Almanza
Edward P K Tsang
Citations