3-3FS: ensemble method for semi-supervised multi-label feature selection.
Abdelouahid AlalgaKhalid BenabdeslemDou El Kefel MansouriPublished in: Knowl. Inf. Syst. (2021)
Keyphrases
- multi label
- feature selection
- text categorization
- ensemble methods
- multilabel classification
- semi supervised
- text classification
- feature subset
- multi label classification
- multi label learning
- semi supervised learning
- labeled data
- random forest
- binary classification
- unlabeled data
- ensemble learning
- random forests
- prediction accuracy
- image classification
- graph cuts
- pairwise
- machine learning methods
- image annotation
- learning tasks
- decision trees
- feature set
- supervised learning
- machine learning
- benchmark datasets
- multi class
- feature space
- multiple labels
- naive bayes
- unsupervised learning
- neural network
- class labels
- ensemble classifier
- support vector
- classification accuracy
- active learning
- base learners
- base classifiers
- support vector machine
- dimensionality reduction
- k nearest neighbor
- knn
- higher order
- feature extraction
- training data
- cost sensitive
- model selection
- transfer learning
- support vector machine svm