The Strength of Weak Learnability
Machine Learning
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A hybrid nearest-neighbor and nearest-hyperrectangle algorithm
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
PAC model-free reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Case-Based Collective Inference for Maritime Object Classification
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Variance Analysis in Software Fault Prediction Models
ISSRE '09 Proceedings of the 2009 20th International Symposium on Software Reliability Engineering
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Classifying program modules as fault-prone or not fault-prone is a valuable technique for guiding the software development process, so that resources can be allocated to components most likely to have faults. The rule-based classification and the case-based learning techniques are commonly used in software quality classification problems. However, studies show that these two techniques share some complementary strengths and weaknesses. Therefore, in this paper we propose a new multi-strategy classification model, RB2CBL, which integrates a rule-based (RB) model with two case-based learning (CBL) models. RB2CBL possesses the merits of both the RB model and CBL model and restrains their drawbacks. In the RB2CBL model, the parameter optimization of the CBL models is critical and an embedded genetic algorithm optimizer is used. Two case studies were carried out to validate the proposed method. The results show that, by suitably choosing the accuracy of the RB model, the RB2CBL model outperforms the RB model alone without overfitting.