Ensemble learning for software fault prediction problem with imbalanced data
نویسندگان
چکیده
منابع مشابه
An Empirical Study for Software Fault-Proneness Prediction with Ensemble Learning Models on Imbalanced Data Sets
Software faults could cause serious system errors and failures, leading to huge economic losses. But currently none of inspection and verification technique is able to find and eliminate all software faults. Software testing is an important way to inspect these faults and raise software reliability, but obviously it is a really expensive job. The estimation of a module’s fault-proneness is impo...
متن کاملOnline Ensemble Learning for Imbalanced Data Streams
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms. Within th...
متن کاملSoftware Defect Prediction for High-Dimensional and Class-Imbalanced Data
Software quality and reliability can be improved using various techniques during the software development process. One effective method is to utilize software metrics and defect data collected during the software development life cycle and build defect predictors using data mining techniques to estimate the quality of target program modules. Such a strategy allows practitioners to intelligently...
متن کاملSoftware Defect Prediction Using Ensemble Learning Survey
Machine learning is a science that explores the building and study of algorithms that can learn from the data. Machine learning process is the union of statistics and artificial intelligence and is closely related to computational statistics. Machine learning takes decisions based on the qualities of the studied data using statistics and adding more advanced artificial intelligence heuristics a...
متن کاملJPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method
Different types of J-proteins perform distinct functions in chaperone processes and diseases development. Accurate identification of types of J-proteins will provide significant clues to reveal the mechanism of J-proteins and contribute to developing drugs for diseases. In this study, an ensemble predictor called JPPRED for J-protein prediction is proposed with hybrid features, including split ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Electrical and Computer Engineering (IJECE)
سال: 2019
ISSN: 2088-8708,2088-8708
DOI: 10.11591/ijece.v9i4.pp3241-3246