Metrics for Measuring Error Extents of Machine Learning Classifiers.
Hong ZhuIan BayleyMark GreenPublished in: AITest (2022)
Keyphrases
- machine learning
- machine learning algorithms
- machine learning methods
- machine learning approaches
- decision trees
- supervised classification
- feature selection
- support vector
- error rate
- meta learning
- error metrics
- training data
- learning classifier systems
- multiple classifier systems
- support vector machine
- information extraction
- learning algorithm
- naive bayes
- class labels
- text classification
- inductive learning
- evaluation metrics
- classification rate
- training set
- computer science
- svm classifier
- test set
- supervised learning
- linear classifiers
- pattern recognition
- explanation based learning
- ensemble classifier
- computer vision
- roc curve
- artificial intelligence
- classification models
- bias variance decomposition
- error bounds
- learning problems
- learning tasks
- neural network
- feature set
- feature subset
- knowledge acquisition
- training samples
- multiple classifiers
- error measure
- text categorization
- text mining
- bayesian networks
- classification systems
- active learning
- classification method
- feature space
- statistical methods
- kernel methods