LR-SMOTE - An improved unbalanced data set oversampling based on K-means and SVM.
X. W. LiangA. P. JiangT. LiY. Y. XueGuotao WangPublished in: Knowl. Based Syst. (2020)
Keyphrases
- data sets
- k means
- class imbalance
- imbalanced data
- imbalanced data sets
- minority class
- training data
- imbalanced datasets
- support vector machine svm
- support vector
- training set
- class distribution
- unbalanced data
- support vector machine
- knn
- logistic regression
- feature selection
- majority class
- clustering method
- train a support vector machine
- decision boundary
- cost sensitive
- cluster analysis
- machine learning
- clustering algorithm
- hierarchical clustering
- sampling methods
- support vectors
- active learning
- cost sensitive learning
- classification algorithm
- kernel function
- hyperplane
- test set
- spectral clustering
- original data
- svm classifier
- random forest
- training examples
- training dataset
- expectation maximization
- input data
- supervised learning
- feature vectors
- feature space
- decision trees