Development and Performance Evaluation of Support Vector Machine Classifiers for Indian Domestic Tourists

Tourism industry outperformed the rest of the economy in recent years, growing faster than other industries such as manufacturing, financial services and retail. Tourism is gaining prominence in enhancing country's income, but, in India it is still largely unorganized. In this paper, an in-depth investigation of Indian tourists' hospitality preference has been done. Factor analysis was carried out to extract significant factors that affect the intention of accommodation booking of Indian domestic tourist and these tourists are clustered based on their behavior towards similar preference using hierarchical clustering. The results of clustering technique have been employed to train and develop efficient classifier models, namely, multilayer perceptron (MLP), radial basis function (RBF) networks and support vector machine (SVM). The study results showed that the SVM classifiers provide the best classification performance of Indian tourist compared to those given by RBF and MLP based methods.