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題名:銀行電話行銷績效評估與預測--RapidMiner之應用
書刊名:數據分析
作者:邢厂民
作者(外文):Hsing, Han-min
出版日期:2020
卷期:15:1
頁次:頁39-58
主題關鍵詞:資料探勘演算法銀行電話行銷RapidMinerData miningAlgorithmBanksTelemarketing
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:3
  • 點閱點閱:8
期刊論文
1.張國忠、劉娜婷、柯麗蓉、鄭敏媛(20060400)。銀行業客服中心之服務功能對顧客認知價值與行為意向之影響研究。管理與系統,13(2),201-220。new window  延伸查詢new window
2.Izetta, J.、Verdes, P. F.、Granitto, P. M.(2017)。Improved multiclass feature selection via list combination。Expert Systems With Applications,88(C),205-216。  new window
3.Jiang, Y.(2018)。Using Logistic Regression Model to Predict the Success of Bank Telemarketing。International Journal on Data Science and Technology,4(1),35-41。  new window
4.Li, J.、Cheng, K.、Wang, S.、Morstatter, F. F.、Trevino, R. P.、Tang, J.、Liu, H.(2017)。Feature Selection: A Data Perspective。ACM Computing Surveys,50(6)。  new window
5.Moro, S.、Cortez, P.、Rita, P.(2014)。A Data-Driven Approach to Predict the Success of Bank Telemarketing。Decision Support Systems,62,22-31。  new window
會議論文
1.Kohavi, R.(1995)。A Study of Cross-Validation and Boots t rap for Accuracy Estimation and Model Selection。International Joint Conference on Artificial Intelligence。  new window
2.Moro, S.、Laureano, R.、Cortez, P.(2012)。Enhancing Bank Direct Marketing Through Data Mining。The Forty First International Conference of the European Marketing Academy。European Marketing Academy。1-8。  new window
圖書
1.簡禎富、許嘉裕(2014)。資料挖礦與大數據分析。前程文化公司。  延伸查詢new window
2.Maheshwari, A.、徐瑞珠(2017)。認識資料科學的第一本書。碁峰資訊。  延伸查詢new window
3.謝邦昌(2014)。SQL Server資料採礦與商業智慧--適用SQL Server 2014/2012。碁峰資訊。  延伸查詢new window
4.Han, J.、Kamber, M.、Pei, J.(2012)。Data Mining Concepts and Techniques。Elsevier Inc.。  new window
5.Kotu, V.、Deshpande, B.(2015)。Predictive Analytics and Data mining--Concepts and Practice with RapidMiner。Morgan Kaufmann Publishers。  new window
其他
1.謝邦昌(2013)。巨量資料的解決方案及其運用,http://download.microsoft.com/download/C/6/0/C60E2BD08A7C479F851E8B5810C0D70F/20131028BigDataEGandDataMining.pdf。  延伸查詢new window
2.李宏毅(2016)。一天搞懂深度學習,https://www.slideshare.net/tw_dsconf/ss62245351。  延伸查詢new window
3.譚望達(2011)。機器學習中的算法(1)--決策樹模型組合之隨機森林與GBDT,http://www.cnblogs.com/LeftNotEasy/archive/2011/03/07/randomforestandgbdt.html。  延伸查詢new window
4.Asif, M.(2018)。Predicting the Success of Bank Telemarketing using various Classification Algorithms,Örebro University School of Business。,https://www.diva-portal.org/smash/get/diva2:1233529/FULLTEXT01.pdf。  new window
5.Boyle, T.(2019)。Dealing with Imbalanced Data,https://towardsdatascience.com/methods-for-dealing-with-imbalanced-data-5b761be45a18。  new window
6.Castrounis, A.(2016)。Artificial Intelligence, Deep Learning, and Neural Networks, Explained,https://www. kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html。  new window
7.De La Iglesia, B.(2016)。Rule Induction,http://www.cs.unibo.it/~montesi/CBD/Beatriz/Session4 Rule%20Induction.pdf。  new window
8.Koehrsen, W.(2017)。Random Forest Simple Explanation,https://medium.com/@williamkoehrsen/random-forest-simple-explanation-377895a60d2d。  new window
9.Moore, A.(2016)。Tutorial Slides,https://www.autonlab.org/tutorials。  new window
10.Shaw, R.(2017)。Top 10 Machine Learning Algorithms for Beginners,https://www.kdnuggets.com/2017/10/top-10-machine-learning-algorithms-beginners.html。  new window
11.(2018)。UC Business Analytics R Programming Guide--Naïve Bayes Classifier,http://uc-r.github.io/page2/。  new window
 
 
 
 
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