Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7755
Title: Support vector machines with manifold learning and probabilistic space projection for tourist expenditure analysis
Authors: Xu, X
Law, R 
Wu, T
Keywords: SVMs
Tourism data analysis
Manifold learning
ISOMAP
Feature projection
Scale transformation
Data mining
Issue Date: 2009
Publisher: Atlantis Press
Source: International journal of computational intelligence systems, 2009, v. 2, no. 1, p. 17-26 How to cite?
Journal: International Journal of Computational Intelligence Systems 
Abstract: The significant economic contributions of the tourism industry in recent years impose an unprecedented force for data mining and machine learning methods to analyze tourism data. The intrinsic problems of raw data in tourism are largely related to the complexity, noise and nonlinearity in the data that may introduce many challenges for the existing data mining techniques such as rough sets and neural networks. In this paper, a novel method using SVM-based classification with two nonlinear feature projection techniques is proposed for tourism data analysis. The first feature projection method is based on ISOMAP (Isometric Feature Mapping), which is a class of manifold learning approaches for dimension reduction. By making use of ISOMAP, part of the noisy data can be identified and the classification accuracy of SVMs can be improved by appropriately discarding the noisy training data. The second feature projection method is a probabilistic space mapping technique for scale transformation. Experimental results on expenditure data of business travelers show that the proposed method can improve prediction performance both in terms of testing accuracy and statistical coincidence. In addition, both of the feature projection methods are helpful to reduce the training time of SVMs.
URI: http://hdl.handle.net/10397/7755
ISSN: 1875-6883
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