Scheduling IDK classifiers with arbitrary dependences to minimize the expected time to successful classification.
Tarek F. AbdelzaherKunal AgrawalSanjoy K. BaruahAlan BurnsRobert I. DavisZhishan GuoYigong HuPublished in: Real Time Syst. (2023)
Keyphrases
- classification systems
- support vector
- classification algorithm
- classification models
- decision trees
- class labels
- classification method
- classifier combination
- classification process
- classification rate
- svm classifier
- supervised classification
- machine learning algorithms
- classification accuracy
- supervised learning
- training samples
- feature selection
- k nearest neighbour
- classification schemes
- feature set
- sufficient training data
- discriminant functions
- classification procedure
- multiple classifier systems
- multiple classifiers
- accurate classification
- training data
- pattern recognition
- support vector machine svm
- rule based classifier
- support vector machine classifiers
- individual classifiers
- feature extraction
- multiclass classification
- multi class
- machine learning methods
- binary classifiers
- classification decisions
- multi category
- scheduling problem
- majority voting
- nearest neighbor classifier
- classification scheme
- support vector machine
- higher classification accuracy
- nearest neighbour
- naive bayes
- data stream classification
- image classification
- decision boundary
- ensemble classifier
- bayesian classifier
- fold cross validation
- training set
- knn
- machine learning
- imbalanced data sets
- text classification
- probabilistic classifiers
- extracted features
- correctly classified
- scheduling algorithm
- combining classifiers
- decision tree classifiers
- accurate classifiers
- discriminative classifiers
- roc analysis