The Repository Method for Chance Discovery in Financial Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Alma L Garcia-Almanza and Edward P. K. Tsang",
  title =        "The Repository Method for Chance Discovery in
                 Financial Forecasting",
  ISSN =         "0302-9743",
  year =         "2006",
  editor =       "Bogdan Gabrys and Robert J. Howlett and 
                 Lakhmi C. Jain",
  series =       "Lecture Notes in Computer Science",
  volume =       "4253",
  booktitle =    "KES 2006, Proceedings of the 10th International
                 Conference on Knowledge-Based Intelligent Information
                 and Engineering Systems",
  pages =        "30--37",
  address =      "Bournemouth, UK",
  month =        oct # " 9-11",
  publisher =    "Springer-Verlag",
  note =         "Part III",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-46542-1",
  bibsource =    "DBLP,",
  DOI =          "doi:10.1007/11893011_5",
  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

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