The Application of Rough Sets to Sightseeing Expenditures

Abstract

The existing tourism demand forecasting models in tourism are unable to capture useful information from a database with numeric and nonnumeric data. This article presents a new approach that applies the rough set theory to form a forecasting model for sightseeing expenditures in Hong Kong. The rough set theory deals with the classificatory analysis of imprecise, uncertain, or incomplete knowledge (data) by incorporating the classical set theory. Based on officially published tourist sightseeing data, decision rules are generated to represent the relationships between the independent variables and the dependent variable. Experimental results revealed that the forecasting model can classify 94.1% of the testing cases, and that 87.5% of the classified cases were identical to their actual counterparts. There was no significant difference between the actual values and the forecast values. The advantages of using decision rules induced by rough set to forecast sightseeing expenditure were also offered.