A Cost-Sensitive Approach to Enhance the use of ML Classifiers in Software Testing Efforts.
Alexandre Moreira NascimentoLucio Flavio VismariPaulo Sérgio CugnascaJoão Battista Camargo JuniorJorge Rady de Almeida JúniorPublished in: ICMLA (2019)
Keyphrases
- cost sensitive
- software testing
- naive bayes
- test cases
- misclassification costs
- boosting algorithms
- multi class
- software development
- cost sensitive learning
- probability estimates
- decision trees
- cost sensitive classification
- binary classification
- software engineering
- software systems
- feature selection
- test set
- testing process
- class distribution
- training data
- support vector
- binary classifiers
- active learning
- support vector machine
- classifier combination
- text categorization
- text classification
- class imbalance
- test suite
- training set
- classification accuracy
- svm classifier
- classification algorithm
- linear classifiers
- test data
- graph cuts
- feature set
- training examples
- probability estimation
- data sets
- training samples
- reinforcement learning
- bayesian networks
- machine learning
- databases