How to produce complementary explanations using an Ensemble Model

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Wilson Silva (Author) Other; Kelwin Fernandes (Author) Other; Jaime S. Cardoso (Author) FEUP

Conference proceedings International

Title: Proceedings of the International Joint Conference on Neural Networks

Pages: 1-8

2019 International Joint Conference on Neural Networks, IJCNN 2019

14 July 2019 through 19 July 2019

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00R-57P

DOI: 10.1109/ijcnn.2019.8852409

Abstract (EN): In order to increase the adoption of machine learning models in areas like medicine and finance, it is necessary to have correct and diverse explanations for the decisions that the models provide, to satisfy the curiosity of decision-makers and the needs of the regulators. In this paper, we introduced a method, based in a previously presented framework, to explain the decisions of an Ensemble Model. Moreover, we instantiate the proposed approach to an ensemble composed of a Scorecard, a Random Forest, and a Deep Neural Network, to produce accurate decisions along with correct and diverse explanations. Our methods are tested on two biomedical datasets and one financial dataset. The proposed ensemble leads to an improvement in the quality of the decisions, and in the correctness of the explanations, when compared to its constituents alone. Qualitatively, it produces diverse explanations that make sense and convince the experts.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8