Evolving neural networks with maximum AUC for imbalanced data classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6076 LNAI, Issue PART 1, 2010, Pages 335-342

  Lu, Xiaofen ^a   Tang, Ke ^a   Yao, Xin ^a,b  

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

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

AUC; Class imbalance Learning; Differential Evolution; Evolutionary Algorithms; Feed forward Neural Networks; ROC

Indexed keywords

AREA UNDER THE ROC CURVE; AUC; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; DESIGN CLASSIFIERS; DIFFERENTIAL EVOLUTION; EMPIRICAL STUDIES; EVOLVING NEURAL NETWORK; EXISTING METHOD; GRADIENT-BASED METHOD; IMBALANCED DATA; IMBALANCED DATA SETS; MULTI-CLASS; NEIGHBORHOOD SEARCH; OBJECTIVE FUNCTIONS; PERFORMANCE INDICATORS; REAL-WORLD; SELF-ADAPTIVE;

BENCHMARKING; BIOLOGY; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; LEARNING ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 77954581070     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13769-3_41     Document Type: Conference Paper

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