Volume 2, Issue 1, March 2009, Pages 17 - 26
Support Vector Machines with Manifold Learning and Probabilistic Space Projection for Tourist Expenditure Analysis
- Xin Xu, Rob Law, Tao Wu
- Corresponding Author
- Xin Xu
Available Online 1 April 2009.
- https://doi.org/10.2991/jnmp.2009.2.1.3How to use a DOI?
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Xin Xu AU - Rob Law AU - Tao Wu PY - 2009 DA - 2009/04 TI - Support Vector Machines with Manifold Learning and Probabilistic Space Projection for Tourist Expenditure Analysis JO - International Journal of Computational Intelligence Systems SP - 17 EP - 26 VL - 2 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/jnmp.2009.2.1.3 DO - https://doi.org/10.2991/jnmp.2009.2.1.3 ID - Xu2009 ER -