Classification of business travelers using SVMs combined with kernel principal component analysis

Xin Xu, Chun Hung Roberts Law, Tao Wu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Data mining techniques for understanding the behavioral and demographic patterns of tourists have received increasing research interests due to the significant economic contributions of the fast growing tourism industry. However, the complexity, noise and nonlinearity in tourism data bring many challenges for existing data mining techniques such as rough sets and neural networks. This paper makes an attempt to develop a data mining approach to tourist expenditure classification based on support vector machines (SVMs) with kernel principal component analysis. Compared with previous methods, the proposed approach not only makes use of the generalization ability of SVMs, which is usually superior to neural networks and rough sets, but also applies a KPCA-based feature extraction method so that the classification accuracy of business travelers can be improved. Utilizing the primary data collected from an Omnibus survey carried out in Hong Kong in late 2005, experimental results showed that the classification accuracy of the SVM model with KPCA is better than other approaches including the previous rough set method and a GA-based selective neural network ensemble method.
Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings
Number of pages9
Publication statusPublished - 1 Dec 2007
Event3rd International Conference on Advanced Data Mining and Applications, ADMA 2007 - Harbin, China
Duration: 6 Aug 20078 Aug 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4632 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Advanced Data Mining and Applications, ADMA 2007

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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