Ensemble-Based Method for Correcting Global Explanation of Prediction Model

Author:

HAMAMOTO Masaki1,NAMBA Hiroyuki1,EGI Masashi1

Affiliation:

1. Research & Development Group, Hitachi, Ltd.

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Reference28 articles.

1. [1] S.M. Lundberg and S.I. Lee, “A unified approach to interpreting model predictions,” Proc. 31st Conf. Neural Information Processing Systems (NIPS 2017), pp.4768-4777, Dec. 2017.

2. [2] S.M. Lundberg, G. Erion, H. Chen, A. DeGrave, J.M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.I. Lee, “From local explanations to global understanding with explainable AI for trees,” Nat. Mach. Intell. vol.2, pp.56-67, Jan. 2020. 10.1038/s42256-019-0138-9

3. [3] L. Breiman, “Statistical modeling: The two cultures,” Statistical Science vol.16, no.3, pp.199-231, Aug. 2001. 10.1214/ss/1009213726

4. [4] J. Wang and Q. Tao, “Machine learning: The state of the art,” IEEE Intell. Syst, vol.23, no.6, pp.49-55. Nov.-Dec. 2008. 10.1109/MIS.2008.107

5. [5] L. Semenova, C. Rudin, and R. Parr, “A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning,” arXiv preprint arXiv:1908.01755, 2020.

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