Hybrid genetic algorithms and case-based reasoning systems

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 725
  • Download : 7
Case-based reasoning (CBR) has been applied to various problem-solving areas for a long time because it is suitable to complex and unstructured problems. However, the design of appropriate case retrieval mechanisms to improve the performance of CBR is still a challenging issue. In this paper, we encode the feature weighting and instance selection within the same genetic algorithm (GA) and suggest simultaneous optimization model of feature weighting and instance selection. This study applies the novel model to corporate bankruptcy prediction. Experimental results show that the proposed model outperforms other CBR models.
Publisher
Springer Verlag (Germany)
Issue Date
2005
Citation

Computational and Information Science, First International Symposium, CIS 2004, Shanghai, China, December 16-18, 2004. Proceedings, pp.922-927

ISSN
1611-3349
DOI
10.1007/b104566
URI
http://hdl.handle.net/10203/3802
Link
http://www.springerlink.com/content/n8g66eke6eb2xpte/
Appears in Collection
KGSF-Conference Papers(학술회의논문)

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0