Open Access
Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
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Article Number | 01005 | |
Number of page(s) | 7 | |
Section | Network Security System, Neural Network and Data Information | |
DOI | https://doi.org/10.1051/matecconf/201823201005 | |
Published online | 19 November 2018 |
- T Michalski, E Gołebiowska. Taxonomy methods in credit risk evaluation[J]. International Advances in Economic Research, 2(4):409-412 (1996). [CrossRef] [Google Scholar]
- N Dardac. Credit Institutions Management Evaluation using Quantitative Methods[J]. Theoretical & Applied Economics, 2(497): 35-40(2006). [Google Scholar]
- E Brynjolfsson, A Mcafee. Big Data’s Management Revolution[J]. Harvard Business Review, 90(10):60 (2012). [Google Scholar]
- Mitchell. Machine Learning[M]. China Machine Press ;McGraw-Hill Education (Asia), (2003). [Google Scholar]
- N.S Altman, “An introduction to kernel and nearest-neighbor nonparametric regression”. The American Statistician. 46 (3): 175–185 (1992). [Google Scholar]
- Rish, Irina, An empirical study of the naive Bayes classifier. IJCAI Workshop on Empirical Methods in AI (2001). [Google Scholar]
- “Artificial Neural Networks as Models of Neural Information Processing | Frontiers Research Topic”. Retrieved 2018-02-20. [Google Scholar]
- J R Quinlan, “Simplifying decision trees”. International Journal of Man-Machine Studies. 27 (3): 221(1987). [CrossRef] [Google Scholar]
- R. Quinlan, “Learning efficient classification procedures”, Machine Learning: an artificial intelligence approach, p. 463-482(1983). [Google Scholar]
- P E Utgoff, Incremental induction of decision trees. Machine learning, 4(2), 161-186(1989). [CrossRef] [Google Scholar]
- Y Freund; E R Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting”. Journal of Computer and System Sciences. 55: 119(1997). [Google Scholar]
- L Breiman, “Bagging predictors”. Machine Learning. 24 (2): 123–140(1996). [Google Scholar]
- Shinde, Amit, A Sahu, D Apley, and G Runger. “Preimages for Variation Patterns from Kernel PCA and Bagging.” IIE Transactions, Vol.46, Iss.5(2014). [Google Scholar]
- Ho, T Kam, Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, pp. 278–282(1995). [Google Scholar]
- Ho T Kam, “The Random Subspace Method for Constructing Decision Forests”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (8): 832–844(1998). [Google Scholar]
- L Rokach; O Maimon, Data mining with decision trees: theory and applications. World Scientific Pub Co Inc (2008). [Google Scholar]
- L Breiman; J H Friedman.; R A Olshen; C J Stone, Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software (1987). [Google Scholar]
- J Gareth; D Witten; T Hastie; R Tibshirani, An Introduction to Statistical Learning. New York: Springer. p. 315 (2015). [Google Scholar]
- D Opitz.; R Maclin, “Popular ensemble methods: An empirical study”. Journal of Artificial Intelligence Research.(1999). [Google Scholar]
- L Breiman, “Random Forests”. Machine Learning. 45 (1): 5–32(2001). [Google Scholar]
- T Hastie; R Tibshirani; J Friedman, The Elements of Statistical Learning (2nd ed.). Springer(2008). [Google Scholar]
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