Artificial neural networks with evolutionary instance selection for financial forecasting

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Abstract

In this paper, I propose a genetic algorithm (GA) approach to instance selection in artificial neural networks (ANNs) for financial data mining. ANN has preeminent learning ability, but often exhibit inconsistent and unpredictable performance for noisy data. In addition, it may not be possible to train ANN or the training task cannot be effectively carried out without data reduction when the amount of data is so large. In this paper, the GA optimizes simultaneously the connection weights between layers and a selection task for relevant instances. The globally evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, genetically selected instances shorten the learning time and enhance prediction performance. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in ANN.

Original languageEnglish
Pages (from-to)519-526
Number of pages8
JournalExpert Systems with Applications
Volume30
Issue number3
DOIs
StatePublished - Apr 2006

Keywords

  • Artificial neural networks
  • Financial forecasting
  • Genetic algorithms
  • Instance selection

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