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2006

Artificial neural networks with evolutionary instance selection for financial forecasting

11 years 9 months ago
Artificial neural networks with evolutionary instance selection for financial forecasting
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. q 2005 Elsevier Ltd. All rights reserved.
Kyoung-jae Kim
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where ESWA
Authors Kyoung-jae Kim
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