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      Adaptive fuzzy rule-based classification system integrating both expert knowledge and data

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      Author
      Tang, Wenyin
      Mao, Kezhi
      Mak, Lee Onn
      Ng, Gee Wah
      Date of Issue
      2012
      Conference Name
      IEEE International Conference on Tools with Artificial Intelligence (24th : 2012 : Athens, Greece)
      School
      School of Electrical and Electronic Engineering
      Abstract
      This paper presents an adaptive fuzzy rule-based classification system using a new hybrid modeling method that integrates both expert knowledge and new knowledge learnt from data. Inspired by human learning, the membership functions of fuzzy rules are optimized based on a hybrid error function that combines errors caused by the class predefined by expert knowledge and nearby historical data. The weights of the two errors can be adjusted by a conservative parameter. Experimental results show that our method significantly reduces classification ambiguity in 9 datasets.
      Subject
      DRNTU::Engineering::Electrical and electronic engineering
      Type
      Conference Paper
      Collections
      • EEE Conference Papers
      http://dx.doi.org/10.1109/ICTAI.2012.114
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