A danger theory inspired artificial immune algorithm for on-line supervised two-class classification problem

Neurocomputing

Volumn 73, Issue 7-9, 2010, Pages 1244-1255

  Zhang, Chenggong ^a   Yi, Zhang ^b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b SICHUAN UNIVERSITY   (China)

Author keywords

Artificial immune system; Computational intelligence; Danger theory; Supervised classification

Indexed keywords

ARTIFICIAL DATA; ARTIFICIAL IMMUNE ALGORITHMS; ARTIFICIAL IMMUNE SYSTEM; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; COMPUTATIONAL INTELLIGENCE; DANGER THEORIES; ENGINEERING APPLICATIONS; FUNDAMENTAL MODELS; GENERALIZATION CAPABILITY; IMMUNE ALGORITHM; LEARNING PROCESS; NEW MODEL; REAL-WORLD PROBLEM; SUPERVISED CLASSIFICATION; THEORETICAL IMMUNOLOGY; TWO-CLASS CLASSIFICATION PROBLEMS;

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); IMMUNOLOGY; LEARNING ALGORITHMS;

COMPUTATIONAL EFFICIENCY;

ACCURACY; ARTICLE; ARTIFICIAL INTELLIGENCE; CLASSIFICATION ALGORITHM; COMPUTER SIMULATION; IMMUNE SYSTEM; LEARNING; MATHEMATICAL ANALYSIS; ONLINE SYSTEM; PRIORITY JOURNAL; PROBLEM SOLVING; THEORETICAL STUDY;

EID: 77649237173     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.01.005     Document Type: Article

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