The Strength of Weak Learnability
Machine Learning
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Combining predictors: comparison of five meta machine learning methods
Information Sciences: an International Journal
An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
Comparing case-based reasoning classifiers for predicting high risk software components
Journal of Systems and Software
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Modern Information Retrieval
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Facility location selection using fuzzy topsis under group decisions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Predicting defect-prone software modules using support vector machines
Journal of Systems and Software
Mindful: A framework for Meta-INDuctive neuro-FUzzy Learning
Information Sciences: an International Journal
IEEE Transactions on Software Engineering
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Estimating software readiness using predictive models
Information Sciences: an International Journal
Information Sciences: an International Journal
Incremental construction of classifier and discriminant ensembles
Information Sciences: an International Journal
Artificial Intelligence Review
Comparison of weights in TOPSIS models
Mathematical and Computer Modelling: An International Journal
A non-functional requirements tradeoff model in Trustworthy Software
Information Sciences: an International Journal
Editorial: Data mining for software trustworthiness
Information Sciences: an International Journal
Searching for rules to detect defective modules: A subgroup discovery approach
Information Sciences: an International Journal
An integrated risk measurement and optimization model for trustworthy software process management
Information Sciences: an International Journal
Information Sciences: an International Journal
An approach to generalization of fuzzy TOPSIS method
Information Sciences: an International Journal
The Journal of Supercomputing
The Journal of Supercomputing
A study of subgroup discovery approaches for defect prediction
Information and Software Technology
Analytic network process in risk assessment and decision analysis
Computers and Operations Research
Information Sciences: an International Journal
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A variety of classification algorithms for software defect detection have been developed over the years. How to select an appropriate classifier for a given task is an important issue in Data mining and knowledge discovery (DMKD). Many studies have compared different types of classification algorithms and the performances of these algorithms may vary using different performance measures and under different circumstances. Since the algorithm selection task needs to examine several criteria, such as accuracy, computational time, and misclassification rate, it can be modeled as a multiple criteria decision making (MCDM) problem. The goal of this paper is to use a set of MCDM methods to rank classification algorithms, with empirical results based on the software defect detection datasets. Since the preferences of the decision maker (DM) play an important role in algorithm evaluation and selection, this paper involved the DM during the ranking procedure by assigning user weights to the performance measures. Four MCDM methods are examined using 38 classification algorithms and 13 evaluation criteria over 10 public-domain software defect datasets. The results indicate that the boosting of CART and the boosting of C4.5 decision tree are ranked as the most appropriate algorithms for software defect datasets. Though the MCDM methods provide some conflicting results for the selected software defect datasets, they agree on most top-ranked classification algorithms.