The design of polynomial function-based neural network predictors for detection of software defects

Information Sciences

Volumn 229, Issue , 2013, Pages 40-57

  Park, Byoung Jun ^a   Oh, Sung Kwun ^b   Pedrycz, Witold ^c,d  

^a Electronics and Telecommunications Research Institute (ETRI)   (South Korea)

^b Suwon University   (South Korea)

^c UNIVERSITY OF ALBERTA   (Canada)

^d SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

Fuzzy clustering; Imbalanced data; Neural networks; Pattern classification; Software defect; Two class discrimination

Indexed keywords

DESIGN COMPONENT; DESIGN METHODOLOGY; FALSE POSITIVE RATES; FUZZY C MEAN; FUZZY C-MEANS; IMBALANCED CLASS; IMBALANCED DATA; LEARNING METHODS; NEURAL NETWORK PREDICTOR; OBJECTIVE FUNCTIONS; RADIAL BASIS FUNCTION NEURAL NETWORKS; RECEIVER OPERATING CHARACTERISTICS; RECEIVER OPERATING CHARACTERISTICS ANALYSIS; SOFTWARE DEFECTS; SOFTWARE QUALITY; TRUE POSITIVE RATES; TWO-CLASS DISCRIMINATION; WEIGHTED COST FUNCTIONS;

CLUSTERING ALGORITHMS; COMPUTER SOFTWARE SELECTION AND EVALUATION; DEFECTS; DESIGN; FUNCTIONS; FUZZY CLUSTERING; FUZZY SYSTEMS; LEARNING ALGORITHMS; PATTERN RECOGNITION; QUALITY CONTROL; RADIAL BASIS FUNCTION NETWORKS;

NEURAL NETWORKS;

EID: 84873292703     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2011.01.026     Document Type: Article

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