GRACER: Improving the Accuracy of RACER Classifier Using A Greedy Approach.
Peyman HosseiniAlireza BasiriPublished in: CSICC (2022)
Keyphrases
- feature selection
- high accuracy
- fold cross validation
- greedy algorithm
- significantly improves the accuracy
- random selection
- classification scheme
- computational cost
- individual classifiers
- decision trees
- confusion matrix
- highest accuracy
- support vector machine classifier
- high classification accuracy
- classification rate
- prediction accuracy
- search algorithm
- accuracy rate
- false negative
- support vector
- multiple classifiers
- unseen data
- classification accuracy
- leave one out cross validation
- higher classification accuracy
- training data
- roc curve
- receiver operating characteristic curves
- ensemble pruning
- decision tree learners
- feature space
- dynamic programming
- feature reduction
- classification algorithm
- classification process
- computational efficiency
- detection rate