The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Hybrid Classifiers for Financial Multicriteria Decision Making: TheCase of Bankruptcy Prediction
Computational Economics
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
On Building Prediction Systems for Software Engineers
Empirical Software Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analogy-Based Practical Classification Rules for Software Quality Estimation
Empirical Software Engineering
Combining techniques to optimize effort predictions in software project management
Journal of Systems and Software
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Analyzing Software Measurement Data with Clustering Techniques
IEEE Intelligent Systems
Predicting Source Code Changes by Mining Change History
IEEE Transactions on Software Engineering
Mining Version Histories to Guide Software Changes
IEEE Transactions on Software Engineering
Automatic Mining of Source Code Repositories to Improve Bug Finding Techniques
IEEE Transactions on Software Engineering
Using Grey Relational Analysis to Predict Software Effort with Small Data Sets
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
Software field failure rate prediction before software deployment
Journal of Systems and Software
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
An improved methodology on information distillation by mining program source code
Data & Knowledge Engineering
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
Mining software repositories for comprehensible software fault prediction models
Journal of Systems and Software
IEEE Transactions on Software Engineering
Integrating in-process software defect prediction with association mining to discover defect pattern
Information and Software Technology
Estimating software readiness using predictive models
Information Sciences: an International Journal
Information Sciences: an International Journal
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
User preferences based software defect detection algorithms selection using MCDM
Information Sciences: an International Journal
The design of polynomial function-based neural network predictors for detection of software defects
Information Sciences: an International Journal
Editorial: Data mining for software trustworthiness
Information Sciences: an International Journal
Online independent reduced least squares support vector regression
Information Sciences: an International Journal
Computers and Electronics in Agriculture
Statistical analysis of the moving least-squares method with unbounded sampling
Information Sciences: an International Journal
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In this paper, a novel evolutionary programming (EP) based asymmetric weighted least squares support vector machine (LSSVM) ensemble learning methodology is proposed for software repository mining. In this methodology, an asymmetric weighted LSSVM model is first proposed. Then the process of building the EP-based asymmetric weighted LSSVM ensemble learning methodology is described in detail. Two publicly available software defect datasets are finally used for illustration and verification of the effectiveness of the proposed EP-based asymmetric weighted LSSVM ensemble learning methodology. Experimental results reveal that the proposed EP-based asymmetric weighted LSSVM ensemble learning methodology can produce promising classification accuracy in software repository mining, relative to other classification methods listed in this study.