Fuzzy immune approach to biomedical data processing

Author

Unold, Olgierd

Author_Institution

Inst. of Comput. Eng., Control & Robot., Wroclaw Univ. of Technol., Wroclaw, Poland

fYear

2009

fDate

11-14 Oct. 2009

Firstpage

4958

Lastpage

4963

Abstract

Classification is an important data mining task in biomedicine. For easy comprehensibility, rules are preferrable to another functions in the analysis of biomedical data. The aim of this work is to use a new fuzzy immune rule-based classification system for biomedical data. The performance of the proposed approach, in terms of classification accuracy and area under the ROC curve, was compared with traditional classifier schemes: C4.5, Naive Bayes, K*, and Meta END.

Keywords

data mining; fuzzy logic; knowledge based systems; medical administrative data processing; pattern classification; biomedical data processing; data mining; fuzzy immune rule-based classification system; Bioinformatics; Cybernetics; Data mining; Fuzzy control; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Immune system; USA Councils; artificial immune system; data mining; fuzzy logic; machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on

Conference_Location

San Antonio, TX

ISSN

1062-922X

Print_ISBN

978-1-4244-2793-2

Electronic_ISBN

1062-922X

Type

conf

DOI

10.1109/ICSMC.2009.5346361

Filename

5346361

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2572721