Title :
GA-parameter optimisation of evolving connectionist systems for classification and a case study from bioinformatics
Author :
Kasabov, Nikola ; Song, Qun
Author_Institution :
Knowledge Eng. & Discovery Res. Inst., Auckland Univ. of Technol., New Zealand
Abstract :
The paper describes an algorithm for parameter optimisation of evolving connectionist systems (ECOS) in an offline processing mode. The algorithm is illustrated on a case study of a classification system that uses gene expression data to predict an outcome of a treatment of cancer disease.
Keywords :
genetic algorithms; genetics; medical computing; neural nets; pattern classification; ECOS; GA-parameter optimisation; bioinformatics; cancer disease; case study; classification system; evolving connectionist systems; gene expression data; offline processing mode; parameter optimisation; Bioinformatics; Cancer; Computer aided software engineering; Diseases; Gene expression; Knowledge engineering; Neural networks; Neurons; Paper technology; Predictive models;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1198128
