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Title: Leveraging the trade-off between accuracy and interpretability in a hybrid intelligent system
Authors: Miao, Chunyan
Zhou, You
Wang, Di
Quek, Chai
Tan, Ah-Hwee
Ng, Geok See
Keywords: DRNTU::Engineering::Computer science and engineering
Neural Fuzzy Inference System
Issue Date: 2017
Source: Wang, D., Quek, C., Tan, A.-H., Miao, C., Ng, G. S., & Zhou, Y. (2017). Leveraging the trade-off between accuracy and interpretability in a hybrid intelligent system. 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 55-60. doi:10.1109/SPAC.2017.8304250
Abstract: Neural Fuzzy Inference System (NFIS) is a widely adopted paradigm to develop a data-driven learning system. This hybrid system has been widely adopted due to its accurate reasoning procedure and comprehensible inference rules. Although most NFISs primarily focus on accuracy, 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 called 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.
DOI: 10.1109/SPAC.2017.8304250
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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