基于机器学习技术的旅游方式偏好研究——以南京市为例  

TOUR-STYLE PREFERENCE ANALYSIS BASED ON MACHINE LEARNING TECHNIQUES——A Case Study on Nanjing Residents

作　　者：张郴[1] 张树夫[2] 陶卓民[2] 

机构地区：[1]南京大学国土资源与旅游学系,南京210093 [2]南京师范大学地理科学学院,南京210046

出　　处：《人文地理》2010年第1期155-160,共6页Human Geography

基　　金：国家社会科学基金项目(07BJY133)

摘　　要：从人口特征和个人价值两方面探讨引起旅游方式偏好差异的关键因素。首次采用新型机器学习算法C4.5-Rule PANE对以南京市民为样本的调查数据进行分析建模,从中获取反映偏好团队游或自助游的典型人群特征的预测规则。研究表明:(1)在人口特征变量中,收入、家庭生命周期、学历对旅游方式偏好起到重要的影响作用;(2)个人价值变量对旅游方式偏好亦起到重要的影响作用;(3)利用机器学习技术建立多维因素到目标概念的非线性映射模型,比仅对一维因素分析更准确、全面;(4)通过建模能够获取反映人们旅游方式偏好的预测规则,该规则从多个角度对偏好团队游或自助游的典型人群特征进行描述。People＇s preferences to tour styles are of practical significance for the development of tourism. In this paper, tour style preferences are investigated systematically by employing the advances in both psychology and machine learning research communities. In detail, team tour and self-help tour are considered in this paper to characterize the potential tourists. Questionnaire is designed based on the demographic characteris- tics and personal values. Data with respect to the variables are extracted from these questionnaires where each variable corresponds to a specific answer in the questionnaire. Then, an advanced special machine learning algorithm named C4.5-rule PANE is employed for data analysis. This algorithm works in a twice-learning style. Specifically, in the first learning stage, this algorithm learns a neural network ensemble from the training data, and the virtual examples are generated and classified by this learned neural network ensemble. In the second learning stage, these virtual examples with the labels provided by the neural network ensemble in the first learning stage are used to enlarge the original training data set and C4.5 decision rules are learned from the augmented data set. The learned model is expressed in the form of decision rules （e.g., Ifa and b then c, where a and b are Boolean expressions with respect to certain variable and c is the concept class to be predicted）, which can be easily understood by the data analyzer. Thus, such a twice-learning procedure produces a predictive model with not only powerful predictive ability for potential tourists but also excellent comprehen- sibility. These advantages enable accurate modeling of the nonlinear mapping from the variables characterizing the potential tourists to the objective concept （i.e. the tour style preferences） and explicit analysis of such mapping from the comprehensible model. This empirical approach is applied to the data extracted from the questionnaire presented to 305 Nanjing residents, and interesting results

关 键 词：旅游方式偏好 机器学习 非线性映射 南京市民 

分 类 号：F592[经济管理—旅游管理]