Classifying Depression in Imbalanced Datasets Using an Autoencoder- Based Anomaly Detection Approach.
Walter GerychEmmanuel AguElke A. RundensteinerPublished in: ICSC (2019)
Keyphrases
- anomaly detection
- imbalanced datasets
- detecting anomalies
- cost sensitive learning
- intrusion detection
- class distribution
- class imbalance
- decision trees
- ensemble methods
- imbalanced data
- sampling methods
- training dataset
- one class support vector machines
- intrusion detection system
- feature selection algorithms
- fraud detection
- machine learning algorithms
- unsupervised learning
- evolutionary algorithm
- neural network
- detect anomalies
- text classification
- pattern recognition
- training data