Using GP to evolve decision rules for classification in financial data sets

Pu WANG, Edward P.K. TSANG, Thomas WEISE, Ke TANG, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

5 Citations (Scopus)


Financial forecasting is a lucrative and complicated application of machine learning. In this paper, we focus on the finding investment opportunities. We therefore explore four different Genetic Programming approaches and compare their performances on real-world data. We find that the novelties we introduced in some of these approaches indeed improve the results. However, we also show that the Genetic Programming process itself is still very inefficient and that further improvements are necessary if we want this application of GP to become successful. © 2010 IEEE.
Original languageEnglish
Title of host publicationProceedings of the 9th IEEE International Conference on Cognitive Informatics, ICCI 2010
Number of pages8
Publication statusPublished - Jul 2010
Externally publishedYes


  • AUC
  • Classification
  • Decision rules
  • Entropy
  • FGP
  • Finance
  • Forecasting
  • Genetic programming


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