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題名：Applying Decision Tree and Neural Network to Raise the Performance of Human Training Quality
書刊名：品質學報
作者：周永燦／吳益銓／林文燦 引用關係
作者(外文)：Jou, Yung-tsan／Wu, Yih-chuan／Lin, Wen-tsann
出版日期：2015
卷期：22:5
頁次：頁383-403
主題關鍵詞：倒傳遞類神經網路；決策樹；Back-propagation neural network；Decision tree；IIP；K-Means；TTQS
原始連結：連回原系統網址new window
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人力資源是組織中最重要的資產。在全球化的競爭趨勢下，人力資源成為公司主要的核心部門，最根本的工作任務就是建立優秀的人力培訓系統以提高訓練品質。台灣訓練品質系統TTQS為協助國內事業單位、訓練機構針對內部人力教育訓練的執行，提供一套完善的教育訓練品質系統，藉由TTQS系統的導入及運作，並依照TTQS制度之PDDRO（Plan、Design、Do、Review、Outcomes）五環程序標準及19項評核指標，評估企業所有人力訓練方案過程及結果之優劣，使執行後的結果與企業績效成為一個系統性的整體規劃，讓人力教育訓練能更符合事業單位的需要。本研究應用資料探勘技術探討人力訓練品質與績效，以2012年TTQS新版評核資料庫為探勘基礎，尋找TTQS之關鍵評核指標項目。首先應用倒傳遞類神經網路評估TTQS資料庫分類之準確率與績效，其訓練樣本與測試樣本預估準確率都高達95%以上。接著比較決策樹演算法（C5.0,CART,CHAID），選擇準確率較高之演算法來探討TTQS關鍵指標，發現C5.0演算法無論在任何資料分割比例下，皆保有較佳之學習準確率，其測試準確率最高達89.41%。K-Means集群分析法用來驗證比較決策樹演算法的結果。經交叉比對K-Means集群分析與決策樹C5.0演算之結果，本研究找出TTQS之重要關鍵評核指標九項。研究結果可幫助台灣之公司企業導入台灣訓練品質系統，掌握TTQS評核關鍵指標內容,強化人力訓練品質與績效。
Human resources are the most important asset of organization. In the trend of globalization, human resources turned to be the core department of a company. The fundamental job is to establish an excellent human training system and enhance training quality. Taiwan TrainQuali System, TTQS provides businesses and training institutions with the tools to carry out internal human training by offering a sound system of evaluating the education and training. The assessment of how good the processes and outcomes of all the training programs in an enterprise by operating the TTQS and following its PDDRO (Plan, Design, Do, Review, Outcomes) procedural standards can integrate the executive results and the enterprise's performance into a systematic integral plan, making the education and training better meet the business needs. This study applies data mining techniques to explore human training quality of 2012 TTQS new version assessment review database and to find TTQS critical assessment indicators. Back-propagation neural networks first apply to evaluate TTQS database classification accuracy and performance. Training patterns and testing patterns prediction accuracy are greater than 95%. Then the study analyzes and compares decision tree algorithms (C5.0, CART, CHAID), and chooses a higher accuracy rate algorithm to discuss TTQS critical indicators. C5.0 has better accuracy rate under any partition proportions and the highest testing accuracy is 89.41%. K-Means clustering analysis is to identify the critical indicators chosen by the decision tree. Through cross comparison with decision tree and K-Means results, this study identifies 9 important critical indicators of TTQS assessment to help enterprise in Taiwan to introduce TTQS, to grasp TTQS assessment critical indicators, and to enhance the quality of human training and performance.
期刊論文
1.Biggs, D.、De Ville, B.、Suen, E.(1991)。A method of choosing multiway partitions for classification and decision trees。Journal of Applied Statistics，18(1)，49-62。  new window
2.Kulatilake, P. H. S. W.、Wu, Q.、Hudaverdi, T.、Kuzu, C.(2010)。Mean particle size prediction in rock blast fragmentation using neural networks。Engineering Geology，114(3/4)，298-311。  new window
3.Lai, J. Z. C.、Liaw, Y.-C.(2008)。Improvement of the K-Means clustering filtering algorithm。Pattern Recognition，41(12)，3677-3681。  new window
4.Lin, W.-T.、Wu, Y.-C.、Tung, C.-L.、Huang, M.-R.、Qin, R.-S.(2010)。Establishing ISO 10015 accreditation system performance model for domestic enterprises。Expert Systems with Applications，37(6)，4119-4127。  new window
5.Rumelhart, D. E.、Widrow, B.、Lehr, M.(1994)。The basic ideas in neural networks。Communications of the ACM，37(3)，87-92。  new window
6.Su, C. T.、Yang, T.、Ke, C. M.(2002)。A neural-network approach for semiconductor wafer postsawing Inspection。IEEE Transactions on Semiconductor Manufacturing，15(2)，260-266。  new window
7.Wang, X.、Niu, R.(2010)。Landslide intelligent prediction using object-oriented method。Soil Dynamics and Earthquake Engineering，30(12)，1478-1486。  new window
8.Wu, J.-D.、Liu, J.-C.(2011)。Development of a predictive system for car fuel consumption using an artificial neural network。Expert Systems with Applications，38(5)，4967-4971。  new window
9.葉怡成、吳沛儒(20090800)。基於類神經網路與交叉驗證法之田口方法。品質學報，16(4)，261-279。new window  延伸查詢new window
10.陳榮靜、林裕証(201012)。以倒傳遞類神經網路改善RFID三度空間定位系統。資訊科技國際期刊，4(2)，44-55。new window  延伸查詢new window
11.林文燦、孔慶瑜、林麗玲(200904)。IIP、ISO10015與TTQS差異分析。品質月刊，45(4)，52-56。  延伸查詢new window
12.Lin, W.-T.、Wang, S.-J.、Wu, Y.-C.、Ye, T.-C.(2011)。An empirical analysis on auto corporation training program planning by Data Mining Techniques。Expert Systems with Applications，38(5)，5841-5850。  new window
13.簡禎富、林昀萱、鄭仁傑(20080600)。建構模糊決策樹及其在有交互作用之半導體資料之資料挖礦以提昇良率之研究。品質學報，15(3)，193-210。new window  延伸查詢new window
14.Vellido, Alfredo、Lisboa, Paulo J. G.、Vaughan, J.(1999)。Neural networks in business: A survey of applications (1992-1998)。Expert Systems with Applications，17(1)，51-70。  new window
15.Kass, G. V.(1980)。An exploratory technique for investigating large quantities of categorical data。Applied Statistics，29(2)，119-127。  new window
會議論文
1.Abidi, S. S.、Ong, J. A.(2000)。A data mining strategy for inductive data clustering: a synergy between self-organising neural networks and K-Means clustering techniques。TENCON 2000，2，568-573。  new window
學位論文
1.Chen, C.-J.(2010)。Prediction and Evaluation of Fitness for Shoe Insert with Artificial Neural Network(碩士論文)。東海大學，Taichung, Taiwan。  new window
2.陳怡婷(2011)。應用資料探勘技術於血液透析動靜脈瘻管阻塞因子之研究(碩士論文)。國立雲林科技大學。  延伸查詢new window
3.Chin, J.-S.(2008)。To Construct the Performance Evaluation Mode for Domestic Enterprises to Carry out ISO 10015 Certification System(碩士論文)。勤益科技大學，Taichung。  new window
4.Hsu, S.-W.(2011)。Application of Neural Network on the Taiwan Training Quali System Impetus of Performance Evaluation(碩士論文)。勤益科技大學，Taichung, Taiwan。  new window
5.葉燦賢(2010)。應用類神經網路模擬設計思考策略(碩士論文)。國立雲林科技大學。  延伸查詢new window
圖書
1.National Association of Small & Medium Enterprises, R.O.C.(2012)。Analytic Report on Visit to IIP, an International Human Resources Quality System。Taipei：Bureau of Employment and Vocational Training, Council of Labor Affairs, Executive Yuan。  new window
2.Yeh, I.-C.(1999)。Application and Practice of Neural Network Models。Taipei：Scholars Books。  new window
3.Yeh, I.-C.(2003)。Application and Practice of Neural Network Models。Taipei：Scholars Books。  new window
4.Yeh, I.-C.(2009)。Application and Practice of Neural Network Models。Taipei：Scholars Books。  new window
5.Berry, M. J. A.、Linoff, G. S.(1997)。Data Mining Technique: For Marketing, Sale, and Customer Support。New York：Wiley。  new window
6.Breiman, L.、Friedman, J. H.、Olshen, R. A.、Stone, C. J.(1984)。Classification and Regression Trees。Chapman & Hall/CRC。  new window
7.Han, Jiawei、Kamber, Micheline(2000)。Data mining: Concepts and techniques。Morgan Kaufmann Publishers。  new window
8.Hastie, T.、Tibshirani, R.、Friedman, J. H.(2001)。The Elements of Statistical Learning, Data Mining, Inference, and Prediction。New York：Springer-Verlag。  new window
 
 
 
 
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