Leveraging the trade-off between accuracy and interpretability in a hybrid intelligent system

• Main Authors: WANG, Di, QUEK, Chai, TAN, Ah-hwee, MIAO, Chunyan, NG, Geok See, ZHOU, You
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: interpretability; neural fuzzy inference system; genetic algorithm; rough set; interpretable rules; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6077; https://ink.library.smu.edu.sg/context/sis_research/article/7080/viewcontent/SPAC2017AI.pdf
• Institution: Singapore Management University

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...