Assessment of factors affecting tourism satisfaction using K-nearest neighborhood and random forest models

被引：3
|
作者：
Tapak, Leili [1 ,2 ]
Abbasi, Hamed [3 ]
Mirhashemi, Hamid [3 ]
机构：
[1] Hamadan Univ Med Sci, Sch Publ Hlth, Dept Biostat, Hamadan 651754171, Iran
[2] Hamadan Univ Med Sci, Modeling Noncommunicable Dis Res Ctr, Hamadan, Iran
[3] Lorestan Univ, Dept Geog, Lorestan, Iran
来源：
BMC RESEARCH NOTES | 2019年 / 12卷 / 01期
关键词：
K-nearest-neighborhood; Random forest; Satisfaction; Tourism;
D O I：
10.1186/s13104-019-4799-6
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Objective: This study aimed to identify factors affecting the satisfaction of tourists traveling to the city of Hamadan as Asian urban tourism capital in 2018. The data a random sample of 300 tourists were collected using a designed questionnaire. We applied random-forest and K-nearest-neighborhood methods to analyze the data. Results: The variables of society behavior, municipal equipment and cost of services were the three top rank variables in predicting tourist satisfaction. Considering the capacity of the ancient city of Hamadan for tourism, policymakers can use our results in planning for providing a sustainable development and flourishing tourism industry in this city.
引用
收藏
页数：5
相关论文
共 50 条
  • [1] Assessment of factors affecting tourism satisfaction using K-nearest neighborhood and random forest models
    Leili Tapak
    Hamed Abbasi
    Hamid Mirhashemi
    [J]. BMC Research Notes, 12
  • [2] Spatial assessment of PM10 hotspots using Random Forest, K-Nearest Neighbour and Naive Bayes
    Tella, Abdulwaheed
    Balogun, Abdul-Lateef
    Adebisi, Naheem
    Abdullah, Samsuri
    [J]. ATMOSPHERIC POLLUTION RESEARCH, 2021, 12 (10)
  • [3] Low visibility event prediction using random forest and K-nearest neighbor methods
    Saleh H. Alhathloul
    Ashok K. Mishra
    Abdul A. Khan
    [J]. Theoretical and Applied Climatology, 2024, 155 : 1289 - 1300
  • [4] Low visibility event prediction using random forest and K-nearest neighbor methods
    Alhathloul, Saleh H.
    Mishra, Ashok K.
    Khan, Abdul A.
    [J]. THEORETICAL AND APPLIED CLIMATOLOGY, 2024, 155 (02) : 1289 - 1300
  • [5] A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping
    Avand, Mohammadtaghi
    Janizadeh, Saeid
    Naghibi, Seyed Amir
    Pourghasemi, Hamid Reza
    Bozchaloei, Saeid Khosrobeigi
    Blaschke, Thomas
    [J]. WATER, 2019, 11 (10)
  • [6] Independent subspace analysis using k-nearest neighborhood distances
    Póczos, B
    Lörincz, A
    [J]. ARTIFICIAL NEURAL NETWORKS: FORMAL MODELS AND THEIR APPLICATIONS - ICANN 2005, PT 2, PROCEEDINGS, 2005, 3697 : 163 - 168
  • [7] On identifying k-nearest neighbors in neighborhood models for efficient and effective collaborative filtering
    Chae, Dong-Kyu
    Lee, Sang-Chul
    Lee, Si-Yong
    Kim, Sang-Wook
    [J]. NEUROCOMPUTING, 2018, 278 : 134 - 143
  • [8] A Study of Travel Time Prediction using K-Nearest Neighborhood Method
    Lim, Sung-Han
    Lee, Hyang-Mi
    Park, Seong-Lyong
    Heo, Tae-Young
    [J]. KOREAN JOURNAL OF APPLIED STATISTICS, 2013, 26 (05) : 835 - 845
  • [9] k-Nearest Neighbor Regressors Optimized by using Random Search
    Ortiz-Bejar, Jose
    Graff, Mario
    Tellez, Eric S.
    Ortiz-Bejar, Jesus
    Cerda Jacobo, Jaime
    [J]. 2018 IEEE INTERNATIONAL AUTUMN MEETING ON POWER, ELECTRONICS AND COMPUTING (ROPEC), 2018,
  • [10] INTRUSION DETECTION SYSTEM USING HYBRID BINARY PSO AND K-NEAREST NEIGHBORHOOD ALGORITHM
    Syarif, Arief Rama
    Gata, Windu
    [J]. PROCEEDINGS OF 2017 11TH INTERNATIONAL CONFERENCE ON INFORMATION & COMMUNICATION TECHNOLOGY AND SYSTEMS (ICTS), 2017, : 181 - 186
12345