Omni-Ensemble Learning (OEL): Utilizing Over-Bagging, Static and Dynamic Ensemble Selection Approaches for Software Defect Prediction

TLEL: A two-layer ensemble learning approach for just-in-time defect prediction

(2017) Xinli Yang et al.   INFORMATION AND SOFTWARE TECHNOLOGY

Software defect prediction using doubly stochastic Poisson processes driven by stochastic belief networks

(2016) Andreas S. Andreou et al.   JOURNAL OF SYSTEMS AND SOFTWARE

A new ensemble learning methodology based on hybridization of classifier ensemble selection approaches

(2015) R. Mousavi et al.   APPLIED SOFT COMPUTING

Software defect prediction using ensemble learning on selected features

(2015) Issam H. Laradji et al.   INFORMATION AND SOFTWARE TECHNOLOGY

A new approach to human microRNA target prediction using ensemble pruning and rotation forest

(2015) Reza Mousavi et al.   Journal of Bioinformatics and Computational Biology

Using Class Imbalance Learning for Software Defect Prediction

(2013) Shuo Wang et al.   IEEE TRANSACTIONS ON RELIABILITY

Data Quality: Some Comments on the NASA Software Defect Datasets

(2013) Martin Shepperd et al.   IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

A Systematic Literature Review on Fault Prediction Performance in Software Engineering

(2011) T. Hall et al.   IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

(2011) M. Galar et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE

A measure of competence based on random classification for dynamic ensemble selection

(2011) Tomasz Woloszynski et al.   Information Fusion

Software fault prediction: A literature review and current trends

(2010) Cagatay Catal   EXPERT SYSTEMS WITH APPLICATIONS

A dynamic classifier ensemble selection approach for noise data

(2010) Jin Xiao et al.   INFORMATION SCIENCES

Learning from Imbalanced Data

(2009) Haibo He et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

A systematic and comprehensive investigation of methods to build and evaluate fault prediction models

(2009) Erik Arisholm et al.   JOURNAL OF SYSTEMS AND SOFTWARE

Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem

(2008) Cagatay Catal et al.   INFORMATION SCIENCES

Predicting defect-prone software modules using support vector machines

(2007) Karim O. Elish et al.   JOURNAL OF SYSTEMS AND SOFTWARE