Classifier uncertainty: evidence, potential impact, and probabilistic treatment.
Niklas TötschDaniel HoffmannPublished in: PeerJ Comput. Sci. (2021)
Keyphrases
- potential impact
- uncertain data
- belief functions
- combination rule
- probability theory
- evidence theory
- dempster shafer
- decision theory
- conditional probabilities
- handling uncertainty
- training data
- dempster shafer theory
- probabilistic model
- dempster shafer evidence theory
- probability measures
- generative model
- support vector
- evidential reasoning
- support vector machine
- classification process
- bayesian networks
- learning algorithm
- empirical evidence
- imprecise probabilities
- classification rate
- posterior probability
- classifier ensemble
- probabilistic knowledge
- training set
- nearest neighbor classifier
- decision trees
- feature space
- classification algorithm
- prior probabilities
- possibility theory
- linear classifiers
- training samples
- classifier combination
- probability measure
- probability distribution
- classification scheme
- feature extraction
- bayes rule
- roc curve
- knowledge about the world
- utility function
- neural network