Comparing Automated Machine Learning Against an Off-the-Shelf Pattern-Based Classifier in a Class Imbalance Problem: Predicting University Dropout.
Leonardo Cañete-SifuentesVictor RoblesErnestina MenasalvasRaúl Monroy BorjaPublished in: IEEE Access (2023)
Keyphrases
- class imbalance
- cost sensitive learning
- machine learning
- majority class
- active learning
- feature selection
- binary classification problems
- minority class
- class distribution
- cost sensitive
- imbalanced datasets
- support vector machine
- learning algorithm
- training data
- decision trees
- small disjuncts
- training set
- highly skewed
- concept drift
- high dimensionality
- sampling methods
- machine learning methods
- training examples
- misclassification costs
- classification algorithm
- machine learning algorithms
- training samples
- svm classifier
- data analysis
- feature set
- feature space
- class labels
- imbalanced class distribution
- pattern recognition
- learning process
- multi class
- supervised learning
- learning tasks
- data mining
- linear classifiers
- decision boundary
- classification accuracy
- text mining
- text categorization
- support vector
- reinforcement learning
- bayesian networks
- feature extraction