薪資管理一直是人力資源領域中的重要議題，一方面求職者希望為自己獲得優渥待遇，另一方面企業欲使用最少的成本獲得高價值人才，但薪資考量因素難以量化，尚無有效的評估方法，導致雙方對薪資認知差異，因此薪資管理議題需藉由完整的大量資料提供，透過人力銀行大數據分析建構客觀的薪資管理模式，達到更謹慎的管理效果。本研究目的為發展薪資管理資料挖礦與大數據分析架構，整合隨機森林（randon forest, RF）與決策樹（decision tree, DT）技術建立薪資管理模式發掘各職務薪資的影響因素，並與台灣某指標性人力銀行網站合作，進行實證研究，透過歷史數據以平均絕對誤差（mean absolute error, MAE）檢驗方法效度，提升預測準確度。數據評估結果顯示此140類職務準確度平均可有效增加，在四項屬性中最高學歷提升為8.67%、工作地提升為8.30%、公司規模提升為9.08%、產業別提升為9.61%，整體提升準確度8.92%。

Compensation management has been an important topic in the field of human resources. Job seekers want to get generous benefits for themselves, on the other hand companies want to use the least amount of cost to obtain high-value talents. However, the considerations of compensation are difficult to quantify. There are currently no effective evaluation method about pay level. It leads cognitive differences between job seekers and companies. Therefore, compensation forecast needs complete a lot of information provided. Through the Job Bank Big Data analytics, construct an objective compensation forecast model can achieve more discreet prediction effect. The study aims to develop a data mining and Big Data analytics framework for compensation forecast. It integrates random forest and decision tree technology and constructs a compensation management models to explore each job impact factors of the compensation. The study cooperates with a Taiwanese indicative Job Bank Web site for empirical research. Through historical data mean absolute error validates the method validity and improve forecast accuracy. The 140 jobs average forecast accuracy can increase effectively. Highest education level promoted 8.67%, job position promoted 8.30%, comapany size promoted 9.08% and industry category promoted 9.61%. It enhances the entirety predictive accuracy 8.92%.

林文政、陳慧娟、周淑儀（2007），台灣資訊電子產業之企業人力資本，薪資與組織績效之關聯性研究¬－薪資中介效果之檢驗，東吳經濟商學學報，59， 57-100。
姚裕群（2008），人力資源開發與管理總論，秀威資訊科技，台北。
胡世忠（2013），雲端時代的殺手級應用：Big Data海量資料分析，天下雜誌，台北。
莊慧玲、蔡勝弘（2006）電子“薪”貴嗎？－電子製造業與傳統製造業受僱員工薪資報酬差異之研究，管理學報，23(1)，1-22。
彭百崇（2000），勞動經濟學，彭百崇，台中。
簡禎富、許嘉裕（2014），資料挖礦與大數據分析，前程文化，台北。
簡禎富、王興仁、陳麗妃（2005），利用資料挖礦提升半導體製造技術員人力資源品質管理，品質學報，12(1)，頁9-28。
簡禎富、林昀萱、鄭仁傑（2008），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)，頁193-210。
簡禎富、游智閔、徐紹鐘（2009），紫式決策分析以建構半導體晶圓廠人力規劃決策模型，管理與系統，16(2)，頁157-180。
Alagukumar, S. and Lawrance, R. (2015), “A Selective Analysis of Microarray Data Using Association Rule Mining,” Procedia Computer Science, 47, 3-12.
Becker, G. (1962), “Investment in human capital: a theoretical analysis,” Journal of Political Economy, 70, 9-49.
Breiman, L. (2001), “Random Forests,” Machine Learning, 45(1), 5-32.
Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984), Classification and Regression Trees, Chapman and Hall, New York.
Chen, F.-H., Chi, D.-J., and Wang, Y.-C. (2015), “Detecting Biotechnology Industry's Earnings Management Using Bayesian Network, Principal Component Analysis, Back Propagation Neural Network, and Decision Tree,” Economic Modelling, 46, 1-10.
Chen, W.-S. and Du, Y.-K. (2009), “Using Neural Networks and Data Mining Techniques for the Financial Distress Prediction Model,” Expert Systems with Applications, 36(2), 4075-4086.
Chen, R., Deng, Z., and Song, Z. (2015), “The Prediction of Malignant Middle Cerebral Artery Infarction: A Predicting Approach Using Random Forest,” Journal of Stroke and Cerebrovascular Diseases, 24(5), 958-964.
Chen, Y.-J., Lin, T.-H., Chang, K.-H., and Chien, Chen-Fu (2013), “Feature Extraction for Defect Classification and Yield Enhancement in Color Filter and Micro-Lens Manufacturing and an Empirical Study,” Journal of Industrial and Production Engineering, 30(8), 510-517.
Chen, W., Li, X., Wang, Y., Chen, G., and Liu, S. (2014), “Forested Landslide Detection Using LiDAR Data and the Random Forest Algorithm: A Case Study of the Three Gorges, China,” Remote Sensing of Environment, 152, 291-301.
Chien, C.-F. and Chen, L.-F. (2007), “Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, 20(4), 528-541.
Chien, C.-F. and Chen, L.-F. (2008), “Data Mining to Improve Personnel Selection and Enhance Human Capital: A Case Study in High-Technology Industry,” Expert Systems with Applications, 34(1), 280-290.
Chien, C.-F. and Chen, L.-F. (2011), “Manufacturing Intelligence for Class Prediction and Rule Generation to Support Human Capital Decisions for High-Tech Industries,” Flexible Services and Manufacturing Journal, 23(3), 263-289.
Chien, Chen-Fu and Chuang, S.-C. (2014), "A Framework for Root Cause Detection of Sub-Batch Processing System for Semiconductor Manufacturing Big Data Analytics," IEEE Transactions on Semiconductor Manufacturing, 27(4), 475-488.
Chien, C.-F., Wang, W.-C., and Cheng, J.-C. (2007), “Data Mining for Yield Enhancement in Semiconductor Manufacturing and an Empirical Study,” Expert Systems with Applications, 33(1), 192-198.
Cutler, A. and Stevens, J.R. (2006), “Random Forests for Microarrays,” Methods in Enzymology, 411, 422-432.
Delgado-Gómez, D., Aguado, D., Lopez-Castroman, J., Santacruz, C., and Artés-Rodriguez, A. (2011), “Improving Sale Performance Prediction Using Support Vector Machines,” Expert Systems with Applications, 38(5), 5129-5132.
Du, P., Samat, A., Waske, B., Liu, S., and Li, Z. (2015), “Random Forest and Rotation Forest for Fully Polarized SAR Image Classification Using Polarimetric and Spatial Features,” ISPRS Journal of Photogrammetry and Remote Sensing, 105, 38-53.
Garavan, T. N. (1997), “Training, development, education and learning: different or the same?” Journal of European Industrial Training, 21(2), 39-50.
Goto, Y., Maeda, T., and Goto, Y. (2013), “Decision Tree Model in Children With Out-of-Hospital Cardiac Arrest for Predicting Neurological Outcomes at the Emergency Department,” Critical Care, 17(4), 133-141.
Harvey, R. J. (1991), Job analysis. Palo Alto, CA.
Hsu, C.-H. (2009), “Data Mining to Improve Industrial Standards and Enhance Production and Marketing: An Empirical Study in Apparel Industry,” Expert Systems with Applications, 36(3), 4185-4191.
Hao, Z., Guangli, X., Yuhuan, Z., and Yilong, L. (2012), “Salary Management System for Small and Medium-Sized Enterprises,” Physics Procedia, 24, 2255-2260.
Hosseini, S.M.S., Maleki, A., and Gholamian, M.R. (2010), “Cluster Analysis Using Data Mining Approach to Develop CRM Methodology to Assess the Customer Loyalty,” Expert Systems with Applications, 37(7), 5259-5264.
Iles, P. (1998), Managing Staff Selection and Assessment. McGraw-Hill Education, UK.
Idris, A., Rizwan, M., and Khan, A. (2012), “Churn Prediction in Telecom Using Random Forest and PSO Based Data Balancing in Combination with Various Feature Selection Strategies,” Computers and Electrical Engineering, 38(6), 1808-1819.
Juan, L. (2009), “Early Warning Model Research of State-Owned Enterprises' Human Capital Risks Based on Improved Neural Network,” BioMedical Information Engineering, 2009. FBIE 2009. International Conference on Future, Sanya, China.
Kass, G.V. (1980), “An Exploratory Technique for Investigating Large Quantities of Categorical Data,” Applied Statistics, 29(2), 119-127.
Kleissner, C. (1998), “Data Mining for the Enterprise,” IEEE Proceedings 31st Annual Hawaii International Conference on System Sciences, 17, 295-304.
Kovalerchuk, B. and Vityaev, E. (2000), Data Mining in Finance: Advances in Relational and Hybrid Methods, Springer Science and Business Media, New York.
Lam, S. S., Ng, T. W., and Feldman, D. C. (2012), “The relationship between external job mobility and salary attainment across career stages,” Journal of Vocational Behavior, 80(1), 129-136.
Lee, Y.-T. (2010), “Exploring High-Performers’ Required Competencies,” Expert Systems with Applications, 37(1), 434-439.
Lee, X.-H. (2013), “Using “Random Forest” for Classification and Regression,” Chinese Journal of Applied Entomology, 50, 1190-1197.
Liu, J. (2003), “An Integrated Perspective of Wage Determination,” Boundless Treasure Academic Journal, (5), 101-135.
Liu, H., Dai, S. and Jiang, H. (2009), “Application of Rough Set and Support Vector Machine in Competency Assessment,” Bio-Inspired Computing, 2009. BIC-TA '09. Fourth International Conference on, Beijing, China.
Lin, W.-T., Wang, S.-J., Wu, Y.-C., and 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.
Mehran, H. (1995), “Executive compensation structure, ownership, and firm performance,” Journal of financial economics, 38(2), 163-184.
Matlay, H. (1999), “Employee relations in small firms: A micro-business perspective,” Employee relations, 21(3), 285-295.
Malekipirbazari, M. and Aksakalli, V. (2015), “Risk Assessment in Social Lending via Random Forests,” Expert Systems with Applications, 42(10), 4621-4631.
Noe, R. (2006), Human Resource Management: Gaining a Competitive Advantage (5th edition). McGraw-Hill, Boston.
Otley, D. (1999), “Performance management: a framework for management control systems research,” Management accounting research, 10(4), 363-382.
O’Keefe, S. and Wang, T.C. (2013), “Publishing Pays: Economists’ Salaries Reflect Productivity,” The Social Science Journal, 50(1), 45-54.
Ólafsson, E. B., Peterson, C. H., and Ambrose Jr, W. G. (1994). “Does recruitment limitation structure populations and communities of macro-invertebrates in marine soft sediments: the relative significance of pre-and post-settlement processes,” Oceanography and marine biology: An annual review, 32, 65-109.
Pedreshi, D., Ruggieri, S., and Turini, F. (2008), “Discrimination-Aware Data Mining,” In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, USA.
Quinlan, J.R. (1986), “Induction of Decision Trees,” Machine Learning, 1(1), 81-106.
Quinlan, J.R. (1993), C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, CA.
Ramesh, B. (2001), “GPR: A Data Mining Tool Using Genetic Programming,” Communications of the Association for Information Systems, 5(1), 1-36.
Rahman, R.M. and Hasan, F.R.M. (2011), “Using and Comparing Different Decision Tree Classification Techniques for Mining ICDDR, B Hospital Surveillance Data,” Expert Systems with Applications, 38(9), 11421-11436.
Romero, C., Ventura, S., and García, E. (2008), “Data Mining in Course Management Systems: Moodle Case Study and Tutorial,” Computers and Education, 51(1), 368-384.
Rygielski, C., Wang, J.C., and Yen, D.C. (2002), “Data Mining Techniques for Customer Relationship Management,” Technology in Society, 24(4), 483-502.
Schultz, T. W. (1961), “Investment in human capital,” The American economic review, 1-17.
Strohmeier, S. and Piazza, F. (2013), “Domain Driven Data Mining in Human Resource Management: A Review of Current Research, ”Expert Systems with Applications, 40(7), 2410-2420.
Sivaram, N. and Ramar, K. (2010), “Applicability of Clustering and Classification Algorithms for Recruitment Data Mining,” International Journal of Computer Applications, 4(5), 23-28.
Sanzana, M.B., Garrido, S.S., and Poblete, C.M. (2015), “Profiles of Chilean Students According to Academic Performance in Mathematics: An Exploratory Study Using Classification Trees and Random Forests,” Studies in Educational Evaluation, 44, 50-59.
Sanmiquel, L., Rossell, J.M., and Vintró, C. (2015), “Study of Spanish Mining Accidents Using Data Mining Techniques,” Safety science, 75, 49-55.
Takahashi, A. and Takahashi, S. (2011), “Gender Salary Differences in Economics Departments in Japan,” Economics of Education Review, 30(6), 1306-1319.
Valle, M.A., Varas, S., and Ruz, G.A. (2012), “Job Performance Prediction in a Call Center Using a Naive Bayes Classifier,” Expert Systems with Applications, 39(11), 9939-9945.
Zhao, X. (2008), “An Empirical Study of Data Mining in Performance Evaluation of HRM,” In 2008 International Symposium on Intelligent Information Technology Application Workshops, Shanghai, China.