Support vector machines with manifold learning and probabilistic space projection for tourist expenditure analysis

Xin Xu, Chun Hung Roberts Law, Tao Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)17-26
Number of pages10
JournalInternational Journal of Computational Intelligence Systems
Volume2
Issue number1
DOIs
Publication statusPublished - 1 Jan 2009

Keywords

  • Data mining
  • Feature projection
  • ISOMAP
  • Manifold learning
  • Scale transformation
  • SVMs
  • Tourism data analysis

ASJC Scopus subject areas

  • Computer Science(all)
  • Computational Mathematics

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