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

DOI10.1016/J.INS.2011.01.026zbMath1293.68236OpenAlexW1971109640MaRDI QIDQ2510155

Witold Pedrycz, Sung-Kwun Oh, Byoung-Jun Park

Publication date: 1 August 2014

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2011.01.026

zbMATH Keywords

neural networkspattern classificationfuzzy clusteringimbalanced datasoftware defecttwo-class discrimination

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Theory of software (68N99)

Related Items (2)

New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification ⋮ Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification

This page was built for publication: The design of polynomial function-based neural network predictors for detection of software defects