You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Fuzzy Rule-Based Similarity Model Enables Learning from Small Case Bases

Authors:

Ning Xiong

Research group:

Learning and Optimisation

Publication Type:

Journal article

Venue:

Applied Soft Computing

Publisher:

Elsevier

Abstract

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for similarity assessment than traditional similarity metrics. The learning of fuzzy similarity rules is performed by exploiting the case base, which is utilized as a valuable resource with hidden knowledge for similarity learning. A sample of similarity is created from a pair of known cases in which the vicinity of case solutions reveals the similarity of case problems. We do pair-wise comparisons of cases in the case base to derive adequate training examples for learning fuzzy similarity rules. The empirical studies have demonstrated that the proposed approach is capable of discovering fuzzy similarity knowledge from a rather low number of cases, giving rise to the competence of CBR systems to work on a small case library.

Bibtex

@article{Xiong1987,
author = {Ning Xiong},
title = {Fuzzy Rule-Based Similarity Model Enables Learning from Small Case Bases},
volume = {113},
pages = {2057--2064},
month = {June},
year = {2013},
journal = {Applied Soft Computing, in press},
publisher = {Elsevier},
url = {http://www.es.mdu.se/publications/1987-}
}