- 學位論文
混合式資料探勘模型於企業社會責任評等之分析
A Hybrid Data Mining Model in Analyzing Corporate Social Responsibility
摘要
企業社會責任(Corporate Social Responsibility, CSR)於近二十年間受到世人重視，編制企業社會責任報告書也成為國內外企業的趨勢，在瞬息萬變的競爭環境中，企業如何扮演世界公民之角色，在獲利、環境及慈善等方面中取得平衡，將是社會大眾所矚目。然而，以往的相關研究多以量化的角度去分析企業社會責任對於財務績效等面向的表現，資料探勘技術尚未廣泛用於此領域。因此，本研究以資料探勘的視角，探討企業社會責任各面向對企業之重要性，提出一個混合的資料探勘模型—CSFSC模型，包括資料前處理、分類方法及規則擷取技術。其中，資料前處理技術包含基於相關性為基礎之特徵選取技術(Correlation-based Feature Selection, CFS)、合成少數類別技術(Synthetic Minority Over-sampling Technique , SMOTE)及Fuzzy C-Means(FCM)分群演算法，而一對一支援向量機(One-Against-One Support Vector Machine, OAOSVM)則作為多元分類方法，最後，以C5.0決策樹作為規則擷取技術。本研究使用2010年中國大陸A股上市公司企業社會責任報告書為資料來源，來測試本研究提出之模型的效能，其實驗結果顯示CSFSC模型有很好的分類準確率，並可提供清楚且有效的決策規則給予企業決策者，有助於提升企業社會責任之評級。因此，可證明在分析CSR議題方面，CSFSC模型是一個有效模型。
並列摘要
Over the past two decades, Corporate Social Responsibility (CSR) has received worldwide attention. Publication of CSR Reports has become the trend for domestic and foreign enterprises. In the constantly changing competition environment, it will be focus of public attention that how enterprises to play the role of corporate citizenship and to achieve a balance in profit, environmental and charitable activities. However, most of previous quantitative studies of CSR concentrate on traditional statistic approaches. The data mining technique has not been widely explored in this area. Thus, this investigation proposed a hybrid data mining CSFSC model integrating data preprocessing approaches, a classification method, and a rule generation mechanism. The data preprocessing approaches include Correlation-based Feature Selection(CFS), Synthetic Minority Over-sampling Technique (SMOTE) and Fuzzy C-Means (FCM) clustering algorithm. The One-Against-One Support Vector Machine (OAOSVM) method was employed as a classifier for performing multi-classification task. The rule-based learning algorithm C5.0 was utilized to generate rules from the results of OAOSVM model. CSR data collected from China’s Listed Firms in 2010 were employed to examine the performance of the proposed model. The empirical results showed that the designed CSFSC model can yield satisfactory classification accuracy as well as provide rules for decision makers. Therefore, the presented CSFSC model is a feasible and effective alternative in analyzing CSR.