臺灣博碩士論文加值系統

國內電子產業無論是在科技技術或是產品品質都是受到國際的肯定，電子產業的淡旺季也經常帶動國內股市市場的波動，因此，國內的電子產業對台灣的經濟具有極大的影響力。然而，電子產業無論是國內市場或是國際市場都是處於競爭激烈的環境下，所以，企業本身必須具有優秀的經營績效、高營業成長率方能在競爭激烈的環境下擴大市場佔
有率以使企業得以永續經營。
本研究選取國內629家上市櫃電子產業公司在2004年及2005年的財務資料，以OWA粗集分類器方法分別進行驗證影響國內電子產業營業成長率的重要屬性。在OWA運算上，可依所選取屬性的情境指標，動態的調整屬性權重值，將多屬性整合成單一屬性後，再以粗集理論產生分類法則並進一步驗證正確率。本研究選取12個屬性進行實驗，以敏感度分析方法，每次刪減一個屬性，作三次不同屬性個數的實驗，以分析及驗證各項屬性與營業成長率的相關程度。在比較上，運用純粗集理論、決策樹C4.5、貝氏網路及多層感知器等的正確率來比較各方法的正確率。比較結果在三次的敏感度分析，本研究方法所得到的正確率均比其他方法高。

The domestic electronic industry is all received the international affirmation both in the technology and product quality. The electronic industry also frequently leads the fluctuation of the domestic stock market in flourishing or stagnancy season. Therefore, the domestic electronic industry could impact Taiwan economy, and the electronic industry face the intense competition environment in the domestic market and international market. So, enterprise must have oneself outstanding management achievements and high business growth rate that it could expand the market to subsist forever in the
competitive environment intensely.
This study proposes OWA-based rough set classifier to enhance the existing classification methods. The OWA operator, it can adjust the weight of attribute based on the situation conditions, and can aggregate the values of many attributes into aggregated values of single attribute. Then, utilize the aggregated value to product the rules of classification and verify the accuracy rate by rough set classifier. For verification, this research collects the financial data of 629 IPO electronic firms in 2004-2005 years to test and verify the accuracy rate of growth rate for electronic firms by the proposed method. This research uses sensitivity analysis method to experiment, which selects 12 attributes and prune off one attribute once time till 10 attributes. The results show that the proposed method outperforms the listing methods.

中文摘要----------------------------------------------------------------------i
英文摘要---------------------------------------------------------------------ii
誌謝----------------------------------------------------------------------- iii
目錄-------------------------------------------------------------------------iv
表目錄---------------------------------------------------------------------- vi
圖目錄--------------------------------------------------------------------- vii
第一章 緒論-------------------------------------------------------------------1
1.1研究背景與動機------------------------------------------------------1
1.2研究目的------------------------------------------------------------2
1.3研究對象------------------------------------------------------------2
1.4論文架構------------------------------------------------------------2
第二章 文獻探討---------------------------------------------------------------4
2.1國內電子產業經營績效的相關文獻--------------------------------------4
2.2有序加權平均--------------------------------------------------------5
2.2.1有序加權平均的相關文獻-----------------------------------------5
2.2.2 OWA運算子演算法-----------------------------------------------6
2.3粗集理論-----------------------------------------------------------11
2.3.1粗集理論的相關文獻--------------------------------------------11
2.3.2粗集理論演算法------------------------------------------------12
2.3.3粗集理論的簡化------------------------------------------------14
2.4其他資料探勘方法---------------------------------------------------14
2.4.1決策樹(Decision Tree)簡介-------------------------------------15
2.4.2貝氏網路(Bayesian Network)簡介--------------------------------18
2.4.3多層感知器(MultiLayer Perceprton)簡介-------------------------19
第三章 研究方法--------------------------------------------------------------22
3.1研究架構-----------------------------------------------------------22
3.2演算法-------------------------------------------------------------24
3.3電子產業經營績效指標-----------------------------------------------28
第四章 電子產業經營績效個案驗證----------------------------------------------31
4.1個案資料庫簡介-----------------------------------------------------31
4.2選取屬性-----------------------------------------------------------31
4.3 OWA資訊整合值-----------------------------------------------------39
4.3.1設定各屬性的權重值--------------------------------------------39
4.3.2計算資訊整合值------------------------------------------------41
4.4 OWA資訊整合值結合粗集理論-----------------------------------------42
4.5驗證結果與比較-----------------------------------------------------45
4.5.1萃取規則------------------------------------------------------45
4.5.2其他資料探勘方法比較------------------------------------------45
第五章 結論與建議------------------------------------------------------------51
參考文獻---------------------------------------------------------------------53

中文參考文獻
1.王士元(2005)，模糊環境下供應績效衡量之研究，國立台灣科技大學，碩士論文。
2.林富松(1990)，研究發展策略與生產力之關係—台灣資訊電子業之實證研究，國立政治大學，碩士論文。
3.林永裔(2001)，應用模糊理論在大型軟體專案需求分析階段的總體技術風險量化評估，私立逢甲大學，碩士論文。
4.林錫祥(2001)，我國上市上櫃IC設計公司效率評估，國立高雄第ㄧ科技大學，碩士論文。
5.林心瀅(2005)，考量景氣因素下企業經營績效與股價報酬率關聯性之研究—以上市櫃光電產業為例，國立屏東科技大學，碩士論文。
6.金文正(1992)，應用貝氏網路實做診斷型專家系統，國立交通大學，碩士論文。
7.徐孟詩(2001)，我國新興科技產業經營績效之研究—以光電產業為例及財務分析之觀點，國立台灣大學，碩士論文。
8.張育政(2005)，結合約略集合理論與基因演算法於支持向量機之入侵偵測研究，私立中華大學，碩士論文。
9.張結雄，使用主成分分析及貝氏網路方法於離子植入製程之錯誤偵測與診斷,國立交通大學，碩士論文。
10.黃俊霖(2004)，應用約略集理論與模糊理論於臺股指數期貨漲跌幅之預測，國立台灣科技大學，碩士論文。
11.莊文仲(2001)，多層感知等化器-使用進化演算法，國立中央大學，碩士論文。
12.蔣佩玲(2005)，模糊語意決策模式結合基因演算法應用於促銷工具選擇，國立高雄第一科技大學，碩士論文。
13.蔡易達(2000)，運用貝氏網路建立台灣地區高風險上市公司之偵測模型，私立元智大學，碩士論文。
14.劉環(2001)，台灣高科技產業經營績效評估與風險關係之研究—以新竹科學園區為例，私立東吳大學，碩士論文。
15.賴威利(2005)，利用約略集合理論預測燒燙傷患者死亡率，私立南台科技大學，碩士論文。
16.游聲裕(1999)，我國電腦及週邊產業企業經營績效剖析，國立成功大學，碩士論文。
17.顔旭良(1997)，我國高科技產業績效評估與其資源配置特性之研究—以新竹科學工業園區為例，國立成功大學，碩士論文。
18.周文賢(2000)，多變量統計分析，智勝文化事業有限公司。
19.黃俊英(2000)，多變量分析，中國經濟企業研究所出版，第七版。
20.郭敏華(2004)，財務報表分析，智勝文化事業有限公司，初版。
21.RichMall理財精算王網站， http://www.richmall.com.tw，投資理財名詞解釋及運用。

英文參考文獻
22.Barrientos M.A., Vargas J.E.(1998), A framework for analysis of dynamic processes based on Bayesian networks and case-based reasoning. Expert System with Applications 15, pp.287-294.
23.Castillo, E., Gutierrez, J.M., and Hadi, A.S. (1997), Expert Systems and
Probabilistic Network Models, Springer.
24.Cheng J., Bell D., and Liu, W.(1998). Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory, Technical Report, University of Alberta.
25.Cherkauer, K. J. & Shavlik, J. W.(1996), Growing simpler decision trees to
facilitate knowledge discovery. Proceedings of the Second International
Conference on Knowledge Discovery and DataMining ,pp. 315–318.
26.Grzymala-Busse,J.W., Stefanowski,J.(1997), Discretization of numerical attributes by direct use of the LEM2 induction algorithm with interval extension,Proceedings of 6th Symposium intelligent Information Systems, Zakopane, Poland, pp.149-168, IPI PAN Press, Poland.
27.Gmymala-Busse J.W.(1992), LERS - A System for Learning From Examples Based on Rough Sets, R. Slowinski, Editor, trntelligent Decision Support: Handbook of Applications and Advances in Rough Set Theory, Kluwer Academic Publishing, pp.3-18.
28.Jaakkola, F.(1996). A introduction to Bayesian network. Springer.
29.Jerzy W. Grzymala-Busse(2003) , A Comparison of Three Strategies to Rule Induction from Data with Numerical Attributes, Proceedings of the International Workshop on Rough Sets in Knowledge Discovery (RSKD 2003), associated with the European Joint Conferences on Theory and Practice of Software 2003,Warsaw, Poland, pp.132–140.
30.Han, J., Kamber, M.,(2001), Data Mining: Concepts And Techniques, Morgan Kaufmann Publishers, New York.
31.Pawlak, Z.(1982), Rough Sets, Informational Journal of Information and
Computer Sciences, 11: 341-356.
32.Pawlak, Z.(1991), Rough Sets: Theoretical Aspects of Reasoning About Data, Boston: Kluwer Academic Publishers.
33.Pawlak, Z.(2002), Rough Sets and Intelligent Data Analysis, Information
Sciences, Vol. 147, pp.1-12.
34.Quinlan J. R.(1986), Induction of Decision Tree, Machine Learning, pp.81-
106.
35.RSES(2002), http://logic.mimuw.edu.pl/~rses, Logic group in Institute of
mathematics Warsaw university Poland.
36.Sung-II, Cho.(2001). A model for IPO pricing and contract choice decision.
The Quarterly Review of Economics and Finance 41,347-364.
37.Utgoff, P.E. (1996), Decision tree induction based on efficient tree
restructuring. Technical Report 95-18, Department of Computer Science,
University of Massachusetts.
38.Yager, R.R.(1988), Ordered weighted averaging aggregation operators in mult-icriteria decision making, IEEE Trans. Systems Man and Cybernetics,pp.183-190.
39.Yager, R.R.(1992), Applications and extensions of OWA aggregations,
International Journal of Man-Machine Studies, Vol. 37, pp. 102-132.
40.Yager, R.R.(1993), Families of OWA operators, Fuzzy Sets and Systems,
pp.125-148.
41.Yager, R.R.(1994), Dimitar P.F., Parameterized and-like and or-like OWA
operators, International Journal of General Systems, pp.297-316.
42.Yoh-Han Pao.(1989), Adaptive Pattern Recognition and Neural Networks,
Addison-Wesley Publishing Company.