• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
J. Oh, D. Ham, Y. Lee, G. Kim, Sep 2019, Short-term Load Fore- casting Using XGBoost and the Analysis of Hyperpara- meters, Trans. of The Korean Institute of Electrical Engineers, Vol. 68, No. 9, pp. 1073-1078Google Search
2 
C. Molnar, Feb 2019, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, LeanpubGoogle Search
3 
S. Bach et al., July 2015, On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PloS One e0130140DOI
4 
A. Datta, S. Sen, Y. Zick, 2016, Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems, Security and Privacy (SP)DOI
5 
S. Lipovetsky and M. Conklin, Oct 2001, Analysis of regression in game theory approach, Applied Stochastic Models in Business and IndustryDOI
6 
M. Ribeiro, S. Singh, C. Guestrin, Aug 2016, Why should I trust you?: Explaining the predictions of any classifier, KDDDOI
7 
A. Shrikumar, P. Greenside, A. Kundaje, 2017, Learning Important Features Through Propagating Activation Differences, ICMLGoogle Search
8 
E. Štrumbelj, I. Kononenko, Dec 2014, Explaining prediction models and individual predictions with feature contributions, Knowledge and information systems, Vol. 41(3)DOI
9 
Dec 2014, https://github.com/oracle/SkaterGoogle Search
10 
Dec 2014, https://github.com/slundberg/shapGoogle Search
11 
S. Lundberg, S. Lee, Nov 2017, A Unified Approach to Interpreting Model Predictions, NIPSGoogle Search
12 
S. Lundberg, G. Erion, S. Lee, 2018, Consistent Individualized Feature Attribution for Tree Ensembles, arXivGoogle Search
13 
T. Chen, C. Guestrin, XGBoost: A Scalable Tree Boosting System, KDD, Aug. 2016.DOI