臺灣博碩士論文加值系統

近年來旅遊業的蓬勃發展，使的旅遊時的風險漸漸受到重視，因此預測旅遊事故風險就更為重要。藉由知識發現探測出來的旅遊風險知識，不僅能讓旅遊業者在事前找出防範的措施，同時也使旅遊業能永續經營下去。本論文主要使用分類樹並結合迴歸樹模型，由國內、外旅遊所保的旅平險投保資料集中，試圖找出影響旅遊安全的主要及次要風險因素及其區間；進而得出高風險與低風險旅遊團之特徵，以提高旅遊風險意識，並可作為旅平險保單設計的參考依據。研究結果可提供旅遊業從業人員了解主要的影響旅遊安全的風險因素及可能區間，以協助旅遊業採取必要的避險措施，以降低旅遊事故的發生，並配合旅平險保單的設計，以規避旅遊風險所可能產生的巨大損失。

The business of tourism industry has been booming in recent years, and the travel risk is taking into account gradually. How to foresee the risk of travel accident becomes an important issue for the travel industry. The travel risk knowledge acquired by using the knowledge discovery technology can not only help the travel industry business man to prevent the risk events before the accident occurred, but also it can help the travel industry keeping running in the long term. In order to acquire the characteristics of high versus low risk travel groups, this study adopts the classification and regression tree techniques to explore the major/minor factors and values that affect the travel safety from the inbound/outbound travel insurance datasets; so as to elevate the risk sense of traveling and become the reference framework for designing the travel safety insurance policy as well. The results of this work can help the personnel of travel industry to understand the factors and values that affect the travel safety; meanwhile, it can assist the travel industry to take necessary action to avoid the occurring of travel accident, and incorporate the design of travel insurance policies to help the travel industry escaping from the huge losses incurred by travel risk.

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究流程 2
1.3 章節架構 4
1.4 研究限制 5
第二章 文獻探討 6
2.1 知識發現 6
2.2 旅遊風險 6
2.3 資料集與大數據 11
2.4 資料探勘 13
2.5 基因演算法 13
第三章 研究方法 16
3.1 研究架構 16
3.2 研究流程 16
3.3 虛擬變數羅吉斯迴歸樹(EDLRT) 17
第四章 實驗設計與分析 21
4.1 實驗流程 21
4.2 模型建構 24
4.3 實證研究 25
4.3.1 案例2010 25
4.3.2 案例2011 31
4.3.3 案例2012 36
4.3.4 案例2013 41
4.3.5 案例2014 46
4.4 案例分析 50
第五章 結論與建議 53
5.1 結論 53
5.2 建議 53
參考文獻 55
附錄 60

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