Diagnosing Ensemble Few-Shot Classifiers.
Weikai YangXi YeXingxing ZhangLanxi XiaoJiazhi XiaZhongyuan WangJun ZhuHanspeter PfisterShixia LiuPublished in: CoRR (2022)
Keyphrases
- ensemble learning
- multiple classifiers
- classifier ensemble
- ensemble classifier
- majority voting
- ensemble pruning
- training set
- combining classifiers
- decision trees
- training data
- class label noise
- accurate classifiers
- decision tree classifiers
- final classification
- feature selection
- randomized trees
- video sequences
- ensemble methods
- ensemble classification
- individual classifiers
- trained classifiers
- multiple classifier systems
- weak learners
- majority vote
- ensemble members
- base classifiers
- concept drifting data streams
- random forests
- weighted voting
- linear classifiers
- one class support vector machines
- rule induction algorithm
- support vector
- mining concept drifting data streams
- pruning algorithm
- imbalanced data
- detection algorithm
- naive bayes
- weak classifiers
- random forest
- binary classification problems
- neural network
- feature ranking
- classifier combination
- video shots
- class labels
- machine learning algorithms
- feature set
- classification accuracy
- video indexing
- generalization ability
- classification models
- publicly available data sets
- classification algorithm
- svm classifier
- bias variance decomposition
- feature space
- learning algorithm