Active learning for imbalanced data under cold start.
Ricardo BarataMiguel LeiteRicardo PachecoMarco O. P. SampaioJoão Tiago AscensãoPedro BizarroPublished in: ICAIF (2021)
Keyphrases
- imbalanced data
- cold start
- active learning
- class imbalance
- recommender systems
- imbalanced class distribution
- collaborative filtering
- data sparsity
- implicit feedback
- sampling methods
- class distribution
- tag recommendation
- random sampling
- transfer learning
- imbalanced datasets
- user preferences
- linear regression
- training set
- random forest
- unlabeled data
- relevance feedback
- semi supervised
- personalized recommendation
- ensemble methods
- learning algorithm
- training examples
- minority class
- supervised learning
- classification models
- feature selection
- cost sensitive
- labeled data
- support vector machine
- semi supervised learning
- machine learning
- matrix factorization
- cost sensitive learning
- learning process
- recommendation algorithms
- decision boundary
- user behavior
- probabilistic model
- image retrieval
- svm classifier
- generalization error
- prediction accuracy