Name
Title | ||
|---|---|---|
Classification of Business Travelers Using SVMs Combined with Kernel Principal Component Analysis |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73871-8_49 | ADMA |
Keywords | Field | DocType |
neural network,rough set,classification accuracy,ensemble method,tourism data,kpca-based feature extraction method,data mining technique,kernel principal,primary data,data mining approach,component analysis,business travelers,ga-based selective neural network,kernel principal component analysis,support vector machine,data collection,data mining,feature extraction | Statistical learning theory,Data mining,Computer science,Support vector machine,Tourism,Feature extraction,Rough set,Kernel principal component analysis,Hospitality management studies,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
4632 | 0302-9743 | 2 |
PageRank | References | Authors |
0.54 | 7 | 3 |