臺灣博碩士論文加值系統

資訊的分類在企業決策制定裡扮演一個重要的角色。隨著資訊科技的進步，我們可以比從前得到了更多的資料或資訊。然而，在分類問題上，許多任務都有高複雜度跟高維度問題。所以，我們用循序權重運算子(OWA operator)，將多屬性資料融合為單一屬性整合值，且分群整合值於分類問題上。這方法由四個主要步驟組成：(1) 利用逐步回歸選出重要的屬性並排列、(2) 利用循序權重運算子得到單一屬性整合值、 (3) 用K均值法(K-Means)將整合值分群 (4) 預測測試資料的分群。經由處理下列三個資料集來驗證：(1)鳶尾花（Iris）、(2)威斯康辛乳癌與 (3)台灣某中小型企業的關鍵績效指標資料集。由實驗結果可得，本文所提出來的方法可以降低高複雜度跟高維度問題，且在分類準確率上皆比先前一些研究所提出來的方法來的準確。

Information classification plays an important role in decision-making problems. As information technology advances, large amounts of information stored in database. Data preprocessing is necessary because some data is dirty. Many tasks are worked out in high complexity and dimensionality in classification problem. Therefore, we apply ordered weighted averaging (OWA) operator to fusion multi-attribute data into the aggregated values of single attribute, and cluster the aggregated values for classification tasks. The proposed method consists of four steps: (1) use stepwise regression to select and order the important attribute, (2) utilize OWA operator to get aggregated values of single attribute from multi-attribute data, (3) cluster the aggregated values by K-Means method, (4) predict the clusters of testing data. In verification and comparison, three datasets: (1) Iris, (2) Wisconsin-breast-cancer and (3) Key Performance Indicators datasets are conducted by the proposed method. The problems of high complexity and dimensionality are solved and the classification accuracy rate is higher than some existing methods.

摘要 I
ABSTRACT II
致謝 III
Content IV
List of Figures V
List of Tables VI
List of Tables VI
List of Appendix VII
1. Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Objective 2
1.4 Research Limitations 3
1.5 Thesis Organization 3
2. Related works 5
2.1 Order Weighted Averaging, OWA 5
2.1.1 Yager’s OWA 5
2.1.2 Fuller and Majlender’s OWA 6
2.2 Feature Selection 7
2.3 Multiple linear regression 8
2.3.1 Stepwise regression 9
2.3.2 Beta coefficient 10
2.4 Clustering 11
2.4.1 K-Means 11
2.4.2 K-Means algorithm 11
3. Proposed method 13
3.1 Research Framework 13
3.2 The proposed algorithm 16
4. Experiments and comparisons 18
4.1 Iris dataset 18
4.2 Wisconsin-breast-cancer (WBC) dataset 26
4.3 Key Performance Indicators dataset 28
5. Conclusion 30
Reference: 31
Appendix A: 36

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