Weight-Constrained Neural Networks in Forecasting Tourist Volumes: A Case Study

Livieris, Ioannis E.; Pintelas, Emmanuel G.; Kotsilieris, Theodore; Stavroyiannis, Stavros; Pintelas, Panayiotis E.

doi:10.3390/electronics8091005

Cited by 10 publications

(4 citation statements)

References 33 publications

(38 reference statements)

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“…The use of optimization methods can find hyperparameters more efficiently and obtain optimal results in the construction of the internal structures of the model (weights, bias, the first number of neurons). Studies on weight restrictions or limitations [65][66][67][68][69] as an alternative approach to solving the problem of overfitting may be targets for future implementations.…”

Section: Discussionmentioning

confidence: 99%

An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping

et al. 2020

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Extreme learning machines (ELMs) are efficient for classification, regression, and time series prediction, as well as being a clear solution to backpropagation structures to determine values in intermediate layers of the learning model. One of the problems that an ELM may face is due to a large number of neurons in the hidden layer, making the expert model a specific data set. With a large number of neurons in the hidden layer, overfitting is more likely and thus unnecessary information can deterioriate the performance of the neural network. To solve this problem, a pruning method is proposed, called Pruning ELM Using Bootstrapped Lasso BR-ELM, which is based on regularization and resampling techniques, to select the most representative neurons for the model response. This method is based on an ensembled variant of Lasso (achieved through bootstrap replications) and aims to shrink the output weight parameters of the neurons to 0 as many and as much as possible. According to a subset of candidate regressors having significant coefficient values (greater than 0), it is possible to select the best neurons in the hidden layer of the ELM. Finally, pattern classification tests and benchmark regression tests of complex real-world problems are performed by comparing the proposed approach to other pruning models for ELMs. It can be seen that statistically BR-ELM can outperform several related state-of-the-art methods in terms of classification accuracies and model errors (while performing equally to Pruning-ELM P-ELM), and this with a significantly reduced number of finally selected neurons.

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Section: Discussionmentioning

confidence: 99%

An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping

et al. 2020

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“…Livieris et al believe that STR of ethnic minorities is a kind of exploitable resource form based on the basic points of ethnic minorities, which has obvious ethnic characteristics and the attributes of sports. It embodies the unique advantages of ethnic resources, natural resources, human resources, location resources, and sports resources [10]. Gao et al discussed the research background of ethnic minority sports from the perspective of development and put forward their own opinions and feelings on the further research of ethnic minority sports.…”

Section: Str Development Researchmentioning

confidence: 99%

Research on Development Strategy of Ethnic Sports Tourism Resources Based on Stochastic Forest Algorithm

Nie

2022

Mobile Information Systems

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The interactive development mode of combining sports industry with tourism industry has attracted more and more people’s attention and gradually been accepted by people. The organic combination of the two can effectively promote the healthy growth of regional economy. The development and operation mode of minority STRs (sports tourism resources) has become an important topic in the research of sports tourism development. It is not only important for the successful integration of minority traditional sports into tourism industry but also of great significance for promoting the development of sports tourism industry. In this paper, the personalized sports tourism service recommendation system based on multi-objective optimization is studied, and a tourism service combination method based on multi-objective optimization is proposed. This method is based on multi-objective optimization, and the historical data of tourists and their current preferences are considered, respectively. The converted data are used to train the RF (random forest) model offline, and online recommendation only needs to be scored and predicted according to the rules of the RF model. The results show that the online recommendation time of the proposed algorithm is basically below 100 s, which is much lower than that of other recommendation algorithms. The experimental results show that setting the weights of user evaluation information and related tourism information can further improve the matching degree between recommendation results and users’ needs.

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“…Livieris et al [29] proposed a new machine learning prediction model, weight constrained neural networks (WCNNs), to predict tourists' volume in Greece, which outperformed classical neural networks and the state-of-the-art regression models. The findings proved that the model was able to efficiently analyze data and forecast the size of tourists' volume and demand.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia

et al. 2022

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The machine learning approach has been widely used in many areas of studies, including the tourism sector. It can offer powerful estimation for prediction. With a growing number of tourism activities, there is a need to predict tourists’ classification for monitoring, decision making, and planning formulation. This paper aims to predict visitors to totally protected areas in Sarawak using machine learning techniques. The prediction model developed would be able to identify significant factors affecting local and foreign visitors to these areas. Several machine learning techniques such as k-NN, Naive Bayes, and Decision Tree were used to predict whether local and foreign visitors’ arrival was high, medium, or low to these totally protected areas in Sarawak, Malaysia. The data of local and foreign visitors’ arrival to eighteen totally protected areas covering national parks, nature reserves, and wildlife centers in Sarawak, Malaysia, from 2015 to 2019 were used in this study. Variables such as the age of the park, distance from the nearest city, types of the park, recreation services availability, natural characteristics availability, and types of connectivity were used in the model. Based on the accuracy measure, precision, and recall, results show Decision Tree (Gain Ratio) exhibited the best prediction performance for both local visitors (accuracy = 80.65) and foreign visitors (accuracy = 84.35%). Distance to the nearest city and size of the park were found to be the most important predictors in predicting the local tourist visitors’ park classification, while for foreign visitors, age, type of park, and the natural characteristics availability were the significant predictors in predicting the foreign tourist visitors’ parks classification. This study exemplifies that machine learning has respectable potential for the prediction of visitors’ data. Future research should consider bagging and boosting algorithms to develop a visitors’ prediction model.

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Weight-Constrained Neural Networks in Forecasting Tourist Volumes: A Case Study

Cited by 10 publications

References 33 publications

An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping

An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping

Research on Development Strategy of Ethnic Sports Tourism Resources Based on Stochastic Forest Algorithm

Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia

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