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A multi-objective ensemble learning approach based on the non-dominated sorting differential evolution for forecasting currency exchange rates

Dinh, T.T.H. and Vu, V.T. and Bui, T.L. (2016) A multi-objective ensemble learning approach based on the non-dominated sorting differential evolution for forecasting currency exchange rates. In: 8th International Conference on Knowledge and Systems Engineering, KSE 2016, 6 October 2016 through 8 October 2016.

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Abstract

Currency exchange rates forecasting is paid a considerable attention of the researchers in the field of forecasting. The neural network is a well-known tool in machine learning. However, two issues are always interested by the scientists: getting toward to global convergence of extreme solutions and determining the optimal weight of the network. This paper proposes the multi-objective method of ensemble learning techniques based on the non-dominated sorting differential evolution (NSDE, a kind of direction-based methods) for training neural networks and application in Foreign Exchange forecasting problems. Two objectives of the selected model are defined based on the Mean Squared Errors and Diversity respectively, in which we used the concept of fitness-sharing based diversity. We experimented the model on four data sets of currency and compared with some of the others that the research community has announced. Through the performance forecasting indicators to show that our new method gives outstanding forecasting results. © 2016 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Institutes > Institute of Techniques for Special Engineering
Identification Number: 10.1109/KSE.2016.7758036
Uncontrolled Keywords: Economics; Electronic trading; Evolutionary algorithms; Finance; Learning algorithms; Learning systems; Mean square error; Optimization; Sorting; Systems engineering; Currency exchange rates; Differential Evolution; Ensemble learning; Multi-objective evolutionary; nondominated; Forecasting
Additional Information: Conference code: 125115. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9787

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