A novel evolutionary-negative correlated mixture of experts model in tourism demand estimation. (November 2016)

• Record Type: Journal Article
• Authors: Kazemi, S.M.R.; Hadavandi, Esmaeil; Shamshirband, Shahaboddin; Asadi, Shahrokh
• Is Part Of: Computers in human behavior. Volume 64(2016)
• Page Start: 641
• Page End: 655
• Publication Date: 2016-11
• Subjects: Mixture of experts -- Negative correlation learning -- Subspace projection -- Time series estimation -- Tourism demand; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2016.07.004
• Languages: English
• ISSNs: 0747-5632

Abstract

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. Highlights: The mixture of experts (ME) model to handle the nonlinearities and other complexities. To model an accurate tourism demand time series forecasting method. A new ME method referred as LSPME (Lag Space Projected ME). The analysis outcomes exhibits the better estimation accuracy of LSPME.