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研究生:陳怡秀
研究生(外文):Yi-Hsiu Chen
論文名稱:以基因演算集成法預測與分析企業績效-以台灣1000大製造業為例
論文名稱(外文):A GA-based ensemble approach to business performance predictions and analyses for 1000 leading manufacturing companies in Taiwan
指導教授:馬麗菁馬麗菁引用關係
指導教授(外文):Li-Ching Ma
口試委員:馬麗菁陳士杰帥嘉珍
口試委員(外文):Li-Ching MaShi-Jay ChenJia-Jane Shuai
口試日期:2014-06-25
學位類別:碩士
校院名稱:國立聯合大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:65
中文關鍵詞:商業智慧集成法企業績效資料探勘製造業
外文關鍵詞:Business intelligenceensemble approachbusiness performancedata miningmanufacturing company
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在二十一世紀的高度競爭與挑戰的時代,企業為了能夠適應市場環境的劇烈變化,以及取得競爭優勢,希望能有效掌握企業經營績效。另一方面,以投資者的角度來說,企業績效亦常為投資決策的主要參考之一,因此企業績效評估與預測,對企業以及投資者都是備受關注的議題。本研究提出基因演算集成法,結合迴歸分析法、決策樹、支持向量機、倒傳遞類神經網路與粒子群演算法五種單一方法,進行企業績效預測,並藉由關聯規則分析表現較佳的企業。本研究以台灣1000大製造業為研究對象,研究結果顯示,本研究所提出的基因演算集成法,預測效果優於拔靴法與多模激發集成法。在多年期分析部分,本研究運用關聯規則找出與高企業績效有高關聯度之變數,分別為稅後純益、獲利率、股東權益報酬率與稅後淨利率。
In the era of high competition and challenge, companies need to grasp business performance efficiently. Business performance prediction is an important issue both for firms to plan corresponding strategies and for investors to make investment decisions. This study proposes an ensemble approach to business performance prediction. The proposed ensemble approach integrates the well-known business intelligence models including regression analysis, decision tree, support vector machine, back-propagation neural network and particle swam optimization. Genetic algorithm is then employed to find the best weights of models in the ensemble approach. In addition, association rules are applied to analyze firms with the best performance. The 1000 leading manufacturing companies in Taiwan are used as an example to demonstrate the proposed approach. The results show that the proposed approach yields the better prediction performance than other ensemble methods. Net profit, profitability ratio, return on equity and net profit margin have strong association with high business performance.
目錄 vi
圖目錄 vii
表目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究流程 3
第二章 文獻探討 4
2.1 企業財務屬性與總體經濟屬性 4
2.2 常用預測方法-單一方法 6
2.3 常用預測方法-集成法 10
2.4 關聯規則分析 12
第三章 研究方法 13
3.1 資料探勘流程 13
3.2 研究模型 17
3.3 研究架構 18
第四章 實證結果 27
4.1 摘要統計分析結果 28
4.2 評估方法 28
4.3 模型分析結果-單一預測模型 28
4.4 模型分析結果-集成預測模型 34
4.5 多年期企業績效變動分析結果 35
4.6 關聯規則分析結果 40
第五章 結論與建議 45
參考文獻 46
附錄一、系統建置與展示 52
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1. 王怡璇、劉宜臻、柯皓仁,2012,『大學圖書館績效評估指標之研究』,圖書館學與資訊科學學刊,第三十八卷‧第一期:43~64頁。
2. 王怡璇、劉宜臻、柯皓仁,2012,『大學圖書館績效評估指標之研究』,圖書館學與資訊科學學刊,第三十八卷‧第一期:43~64頁。
3. 吳貞慧、劉維琪,2006,『台灣上市公司績效與投資人行為偏誤之研究』,財務金融學刊,第十四卷‧第二期:1~39頁。
4. 吳貞慧、劉維琪,2006,『台灣上市公司績效與投資人行為偏誤之研究』,財務金融學刊,第十四卷‧第二期:1~39頁。
5. 李維平、黃郁授、戴彰廷,2008,「自適應慣性權重改良粒子群演算法之研究」,資訊科學應用期刊,第4卷,第1期:123~142頁。
6. 李維平、黃郁授、戴彰廷,2008,「自適應慣性權重改良粒子群演算法之研究」,資訊科學應用期刊,第4卷,第1期:123~142頁。
7. 周中理、李炯東,2009,『網絡關係、創新策略與經營績效關係之研究-以橡膠輸送帶製造案為例』,經營管理論叢,第三屆管理與決策學術研討會特刊:175-194頁。
8. 周中理、李炯東,2009,『網絡關係、創新策略與經營績效關係之研究-以橡膠輸送帶製造案為例』,經營管理論叢,第三屆管理與決策學術研討會特刊:175-194頁。
9. 陳信源、葉鎮源、林昕潔、黃明居、柯皓仁、楊維邦,2009,『結合支援向量機與詮釋資料之圖書自動分類方法』,資訊科技國際期刊,第三卷•第一期:2~21頁。
10. 陳信源、葉鎮源、林昕潔、黃明居、柯皓仁、楊維邦,2009,『結合支援向量機與詮釋資料之圖書自動分類方法』,資訊科技國際期刊,第三卷•第一期:2~21頁。
11. 黃政仁、詹佳樺,2013,『創新能力、創新效率與公司價值:以台灣電子資訊業為例』,商略學報,第五卷‧第一期:1~17頁。
12. 黃政仁、詹佳樺,2013,『創新能力、創新效率與公司價值:以台灣電子資訊業為例』,商略學報,第五卷‧第一期:1~17頁。
13. 黃博怡、張大成、江欣怡,2006,『考慮總體經濟因素之企業危機預警模型』,金融風險管理季刊,第二卷‧第二期:75~89頁。
14. 黃博怡、張大成、江欣怡,2006,『考慮總體經濟因素之企業危機預警模型』,金融風險管理季刊,第二卷‧第二期:75~89頁。
15. 楊朝旭、黃潔,2003,『非財務性資訊之價值攸關性-以銀行業之服務品質為例』,中華管理評論國際學報,第六卷‧第二期:30~46頁。
 
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