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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/67866
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/67866


    Title: 探討三種分類方法來提升混合方式用在兩階段決策模式的準確率:以旅遊決策為例
    Improving the precision rate of the Two-stage Decision Model in the context of tourism decision-making via exploring Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network
    Authors: 陳怡倩
    Chen, Yi Chien
    Contributors: 傅豐玲
    Fu, Fong Lin
    陳怡倩
    Chen, Yi Chien
    Keywords: Classification
    Data-mining
    Two-stage Decision Model
    Multi-staged Binary Tree
    K-nearest Neighbour
    Back Propagation of Error Neural Network
    Date: 2013
    Issue Date: 2014-07-29 16:04:20 (UTC+8)
    Abstract: The two-stage data mining technique for classifications in tourism recommendation system is necessary to connect user perception, decision criteria and decision purpose. In existed literature, hybrid data mining method combining Decision Tree and K-nearest neighbour approaches (DTKNN) were proposed. It has a high precision rate of approximately 80% in K-nearest Neighbour (KNN) but a much lower rate in the first stage using Decision Tree (Fu & Tu, 2011). It included two potential improvements on two-stage technique. To improve the first stage of DTKNN in precision rate and the efficiency, the amount of questions is decreased when users search for the desired recommendation on the system. In this paper, the researcher investigates the way to improve the first stage of DTKNN for full questionnaires and also determines the suitability of dynamic questionnaire based on its precision rate in future tourism recommendation system. Firstly, this study compared and chose the highest precision rate among Decision Tree, Multi-staged Binary Tree and Back Propagation of Error Neural Network (BPNN). The chosen method is then combined with KNN to propose a new methodology. Secondly, the study compared and deter¬mined the suitability of dynamic questionnaires for all three classification methods by decreasing the number of attributes. The suitable dynamic questionnaire is based on the least amount of attributes used with an appropriate precision rate. Tourism recommendation system is selected as the target to apply and analyse the usefulness of the algorithm as tourism selection is a two-stage example. Tourism selection is to determine expected goal and experience before going on a tour at the first stage and to choose the tour that best matches stage one. The result indicates that Multi-staged Bi¬nary Tree has the highest precision rate of 74.167% comparing to Decision Tree with 73.33% then BPNN with 65.47% for full questionnaire. This new approach will improve the effectiveness of the system by improving the precision rate of first stage under the current DTKNN method. For dynamic questionnaire, the result has shown that Decision Tree is the most suitable method given that it resulted in the least difference of 1.33% in precision rate comparing to full questionnaire, as opposed to 1.48% for BPNN and 4% for Multi-staged Binary Tree. Thus, dynamic questionnaire will also improve the efficiency by decreasing the amount of questions which users are required to fill in when searching for the desired recommendation on the system. It provides users with the option to not answer some questions. It also increases the practicality of non-dynamic questionnaire and, therefore, affects the ultimate precision rate.
    Reference: 1. Alippi, C., Piuri, V. & Sami, M. (1995). Sensitivity to errors in artificial neural networks: A behavioral approach. Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on, 42(6), 358-361.
    2. Baloglu, S. & AcCleary, K.W. (1999). A Model of Destination Image Formation. Annals of Tourism Research, 26(4), 868-897.
    3. Black, T.R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement, and statistics. Thousand Oaks, CA: SAGE Publications, Inc. (p. 118).
    4. Chattamvelli R. (2009). Data Mining Methods. Alpha Science.
    5. Chesnut, T.J., Woodward, J. & Wilson, E. (2007). A Comparison of Closed- and Open-Ended Question Formats for Select Housing Characteristics in the 2006 American Community Survey Content Test. U.S. Census Bureau Washington, D.C.20233. Retrieved February 21, 2014, from http://www.fcsm.gov/07papers/Chesnut.VI-A.pdf
    6. Chi, C.G. & Qu, H. (2008). Examining the Structural Relationships of destination Image, Tourist Satisfaction and Destination Loyalty: An integrated Approach. Tourism Management, 29, 624-636.
    7. Chuvakin, A.A., Schmidt, K.J. & Phillips, C. (2012). Logging and log management: The authoritative Guide to Understanding the concepts surrounding logging and log management. Syngeress. Available form Google Books.
    8. Cooper, D.R. & Schindler, P.S. (2011). Business Research Method (Eleventh Edition). McGraw-Hill Education.
    9. Dennett, D.C. (1981). Brainstorms: Philosophical Essays on Mind and Psychology. United States of America: MIT Press edition.
    10. Doyle, B. (2011). Free Will: The Scandal in Philosophy. Cambridge, MA, USA: I-Phi Press. Available from Google Books.
    11. Ekonde, C.N. (2010). Tourism destination marketing: A comparative study, between Gotland Island, Sweden and Limbe city, Cameroon. Retrieved February 13, 2014, from http://www.diva-portal.org/smash/get/diva2:322381/FULLTEXT01.pdf
    12. Explorable.com (2009). Convenience Sampling. Retrieved March 5, 2014, from http://explorable.com/convenience-sampling
    13. Fayyad, U. M. & Irani, K. B. (1990). What Should Be Minimized in a Decision Tree? Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Boston, Massachusetts, 749-754.
    14. Fu, F. & Tu, Y. (2011). Intelligence on Gift Giving Website Based on Hybrid Approach of Decision Tree and Similarity. WASE International Conference on Information Engineering (ICIE), Aug 13-14, Xi'an, China.
    15. Gaur, P. (2012). Neural Networks in Data Mining. International Journal of Electronics and Computer Science Engineering, 1(3), 1449. Retrieved from The University of Auckland database.
    16. Gong, A. & Liu, Y. (2011). Improved KNN Classification Algorithm by Dynamic Obtaining K. In G. Shen & X. Huang (Eds.), Advanced Research on Electronic Commerce, Web Application, and Communication: International Conference, ECWAC 2011, Guangzhou, China, April 16-17, 2011, Proceedings, Part I (pp. 320-324). Retrieved from The University of Auckland database.
    17. Joseph P. & Gilmore, G.H. (1998). Welcome to the Experience Economy. Harvard Business Review, 76(4), 97-105.
    18. Jönsson, C. & Devonish, D. (2008). Does nationality, gender, and age affect travel motivation? A case of visitors to the Caribbean island of Barbados. Journal of Travel & Tourism Marketing, 25(3-4), 398-408.
    19. Lehto, X.Y., O’Leary, J.T. & Morrison, A.M. (2004). The Effect of Prior Experience on Vacation Behaviour. Annals of Tourism Research, 31(4), 801-818.
    20. Martino, B.D., Kumaran, D., Seymour, B. & Dolan, R.J. (2006). Frames, Biases, and Rational Decision-Making in the Human Brain. Science, 313(5787), 684-687.
    21. Meng, F. & Uysal, M. (2008). Effects of gender differences on perceptions of destination attributes, motivations, and travel values: An examination of a nature-based resort destination. Journal of Sustainable Tourism, 16(4), 445-466.
    22. Mitchel T.M. (1997). Machine Learning. McGraw-Hill.
    23. Myers, I.B. (1985). A Guide to the Development and Use of the Myers-Briggs Type Indicator: Manual. Palo Alto, CA: Consulting Psychologists Press.
    24. Peng, L.C., Kao, Y.H., Hung, S.Y., Yeh, Y.Z., Hsiao, H.C., (2011). "Find Fun." Report for National Cheng-Chi University Seminar Presentation.
    25. Quenk, N.L. (2009). Essentials of Myers-Briggs Type Indicator Assessment. (Second Edition). John Wiley & Sons.
    26. QuestionPro. (2007). Developing Dynamic Surveys. Retrieved March 3, 2014, from http://www.questionpro.com/images/bookshelf/dynamicsurveys.pdf
    27. Reja, U., Manfreda, K.L., Hlebec, V. & Vehovar, V. (2003). Open-ended vs. close-ended questions in web questionnaires. Advances in Methodology and Statistics (Metodološki zvezki), 19, 159-77.
    28. Rojas, C. & Camarero, C. (2008). Visitor’s Experience, Mood and Satisfaction in Heritage Context: Evidence from an Interpretation Center. Tourism Management, 29,525-537.
    29. Rust, R.T. & Oliver, R.L. (1994). Service quality: insights and managerial implication from the frontier. In R.T. Rust & R.L. Oliver (Eds.), Service quality: New directions in theory and practice (pp. 1-19). Thousand Oaks, CA: Sage.
    30. Safavin, S.R. & Landgrebe, D. (1991). A survey of decision tree classier methodology. IEEE Transactions on Systems, Man and Cybernetics, 21(3), 660-674.
    31. Seng, C. & Chen, M. (2012). A Study of Experience Expectations of Museum Visitors. Tourism Management, 33, 53-60.
    32. Shmueli G., Patel N.R. & Bruce P.C. (2010). Data Mining For Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner (Second Edition). Wiley.
    33. Snap Surveys. (2014). Dynamic Questionnaires. Retrieved March 3, 2014, from http://www.snapsurveys.com/survey-software/dynamic-questionnaires/
    34. Stackoverflow (2014). Whats is the difference between train, validation and test set, in neural networks? Retrieved March 3, 2014, from http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ
    35. Wang, N. (1999). Rethink Authenticity in Tourism Experience. Annals of Tourism Research, 26(2), 349-370.
    36. Wu, J. & Coggeshall, S. (2012). Foundations of Predictive Analytics. CRC Press. Retrieved from The University of Auckland database.
    37. Yeh, Y.L., Hou, T.H. & Low, C.Y. (2012). The Classification of Children’s Occupational Therapy Problems Using Neural Network. In Qian, Z., Cao, L., Su, W., Wang, T. & Yang, H. (Eds.), Recent Advances in Computer Science and Information Engineering, 1 (pp. 687-692). Retrieved from The University of Auckland database.
    38. Yuksel, A., Yuksel, F. & Bilim, Y. (2010). Destination Attachment: Effects on Customer Satisfaction and Cognitive. Affective and Conative Loyalty. Tourism Management. 31, 274-284.
    39. United Nation World Tourism Organisation (2013). UNWTO Tourism Highlights, 2013 Edition. Retrieved January 7, 2014, from http://dtxtq4w60xqpw.cloudfront.net/sites/all/files/pdf/unwto_highlights13_en_hr.pdf
    Description: 碩士
    國立政治大學
    資訊管理研究所
    101356043
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0101356043
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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