Training Financial Decision Support Systems with Thousands of Decision Rules Using Differential Evolution with Embedded Dimensionality Reduction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)

Abstract

This paper proposes an improvement of the training process of financial decision support systems, where evolutionary algorithms are used to integrate a large number of decision rules. It especially concerns the new computational intelligence approaches that try to replace the expert knowledge with their own artificial knowledge discovered using very large models from very large training datasets, where the large number of decision rules is crucial, because it defines the degree of freedom for the further learning algorithm. The proposed approach focuses on enhancing Differential Evolution by embedding dimensionality reduction to process objective functions with thousands of possibly correlated variables. Experiments performed on a financial decision support system with \(5000\) decision rules tested on \(20\) datasets from the Euronext Paris confirm that the proposed approach may significantly improve the training process.

References

  1. 1.
    Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning - a new frontier in artificial intelligence research. IEEE Comput. Intell. Mag. 5, 13–18 (2010)CrossRefGoogle Scholar
  2. 2.
    Li, D., Hinton, G., Kingsbury, B.: New types of deep neural network learning for speech recognition and related applications: an overview. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8599–8860 (2013)Google Scholar
  3. 3.
    Larranaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. Kluwer Academic Publishers, Boston (2002)CrossRefMATHGoogle Scholar
  4. 4.
    Korczak, J., Lipinski, P.: Evolutionary building of stock trading experts in a real-time system. In: IEEE Congress on Evolutionary Computation, pp. 940–947 (2004)Google Scholar
  5. 5.
    Lipinski, P.: Parallel evolutionary algorithms for stock market trading rule selection on many-core graphics processors. Nat. Comput. Comput. Finance Stud. Comput. Intell. 380, 79–92 (2012)CrossRefGoogle Scholar
  6. 6.
    Sirlantzis, K., Fairhurst, M.C., Guest, R.M.: An evolutionary algorithm for classifier and combination rule selection in multiple classifier systems. In: International Conference on Pattern Recognition, pp. 771–774 (2002)Google Scholar
  7. 7.
    Hilletofth, P., Lattila, L.: Agent based decision support in the supply chain context. Ind. Manage. Data Syst. 112, 1217–1235 (2012)CrossRefGoogle Scholar
  8. 8.
    Ishibuchi, H., Yamamoto, T.: Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst. 141, 59–88 (2004)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Nojima, Y., Ishibuchi, H.: Multiobjective genetic fuzzy rule selection with fuzzy relational rules. In: IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems, pp. 60–67 (2013)Google Scholar
  10. 10.
    Webb, A.: Statistical Pattern Recognition. John Wiley, New York (2002)CrossRefMATHGoogle Scholar
  11. 11.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31 (2011)CrossRefGoogle Scholar
  12. 12.
    Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)MATHGoogle Scholar
  13. 13.
    Murphy, J.: Technical Analysis of the Financial Markets. NUIF, New York (1998)Google Scholar
  14. 14.
    Sharpe, W.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Computer Science, Computational Intelligence Research GroupUniversity of WroclawWroclawPoland

Personalised recommendations