A novel evolutionary-negative correlated mixture of experts model in tourism demand estimation |
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| Affiliation: | 1. Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran;2. Department of Computer System and Information Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;3. Faculty of Engineering, Farabi Campus, University of Tehran, Iran;1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China;2. State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing 100083, China;3. Ocean Engineering Joint Research Center of DUT-UWA, Dalian 116024, China;1. Department of Physics, University of Birjand, P. O. Box 97175-615, Birjand, Iran;2. Department of Electrical Engineering, Technical Faculty of Ferdows, University of Birjand, Iran;1. Programa de Pós-Graduação em Entomologia, Departamento de Entomologia, Universidade Federal de Viçosa, Brazil;2. Departamento de Biologia Geral, Universidade Federal de Viçosa, Brazil;3. Centro de Ciências Agrárias, Departamento de Biologia, Universidade Federal do Espírito Santo Alegre, ES, Brazil |
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| Abstract: | Mixtures of experts (ME) model are widely used in many different areas as a recognized ensemble learning approach to account for nonlinearities and other complexities in the data, such as time series estimation. With the aim of developing an accurate tourism demand time series estimation model, a mixture of experts model called LSPME (Lag Space Projected ME) is presented by combining ideas from subspace projection methods and negative correlation learning (NCL). The LSPME uses a new cluster-based lag space projection (CLSP) method to automatically obtain input space to train each expert focused on the difficult instances at each step of the boosting approach. For training experts of the LSPME, a new NCL algorithm called Sequential Evolutionary NCL algorithm (SENCL) is proposed that uses a moving average for the correlation penalty term in the error function of each expert to measure the error correlation between it and its previous experts. The LSPME model was compared with other ensemble models using monthly tourist arrivals to Japan from four markets: The United States, United Kingdom, Hong Kong and Taiwan. The experimental results show that the estimation accuracy of the proposed LSPME model is significantly better than the other ensemble models and can be considered to be a promising alternative for time series estimation problems. |
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| Keywords: | Mixture of experts Negative correlation learning Subspace projection Time series estimation Tourism demand |
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