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Leveraging the Trade-off between Accuracy and Interpretability in a Hybrid Intelligent System


Authors: D. Wang, C. Quek, A.-H. Tan, C. Miao, G. S. Ng, and Y. Zhou
Title: Leveraging the Trade-off between Accuracy and Interpretability in a Hybrid Intelligent System
Abstract: Neural Fuzzy Inference System (NFIS) is a widely adopted paradigm to develop a data-driven learning system. This hybrid system has been intensively investigated due to its aptitudes in accurate data processing and human interpretable inference rule base. Most NFISs primarily focus on accuracy, but we have observed an ever increasing demand on improving the interpretability of NFISs, and other types of machine learning systems. In this paper, we illustrate how we leverage the trade-off between accuracy and interpretability in an NFIS named Genetic Algorithm and Rough Set Incorporated Neural Fuzzy Inference System (GARSINFIS). In a nutshell, GARSINFIS self-organizes its network structure with a small set of control parameters and constraints. Moreover, its autonomously generated inference rule base tries to achieve higher interpretability without sacrificing accuracy. Furthermore, we demonstrate different configuration options of GARSINFIS using well-known benchmarking datasets. The performance of GARSINFIS on both accuracy and interpretability is shown to be encouraging when compared against other decision tree, Bayesian, neural and neural fuzzy models.
Keywords: Interpretability; Neural fuzzy inference system; Genetic algorithm; Rough set; Interpretable rules
Conference Name: International Conference on Security, Pattern Analysis, and Cybernetics (SPAC'17)
Location: Shenzhen, China
Publisher: IEEE
Year: 2017
Accepted PDF File: Leveraging_the_Trade-off_between_Accuracy_and_Interpretability_in_a_Hybrid_Intelligent_System_accepted.pdf
Permanent Link: https://doi.org/10.1109/SPAC.2017.8304250
Reference: D. Wang, C. Quek, A.-H. Tan, C. Miao, G. S. Ng, and Y. Zhou, “Leveraging the trade-off between accuracy and interpretability in a hybrid intelligent system,” in Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics (SPAC’17). IEEE, December 2017, pp. 55–60.
bibtex: 
@inproceedings{LILY-c140, 
    author	= {Wang, Di and Quek, Chai and Tan, Ah-Hwee and Miao, Chunyan and Ng, Geok See and Zhou, You},
    title	= {Leveraging the Trade-off between Accuracy and Interpretability in a Hybrid Intelligent System},  
    booktitle	= {Proceedings of the International Conference on Security, Pattern Analysis, and Cybernetics (SPAC'17)}, 
    year		= {2017}, 
    month	= {December}, 
    pages	= {55-60}, 
    location	= {Shenzhen, China},
    publisher	= {IEEE},
 }