Adjusting SVMs for Large Data Sets using Balanced Decision Trees.
Cristina VatamanuDragos Teodor GavrilutGeorge PopoiuPublished in: SYNASC (2018)
Keyphrases
- decision trees
- support vector
- training set
- machine learning
- training data
- bayes classifier
- data sets
- predictive accuracy
- decision tree induction
- imbalanced datasets
- feature selection
- logistic regression
- support vector machine svm
- training support vector machines
- support vector machine
- svm classifier
- maximum margin
- multi class
- decision tree learning
- attribute selection
- data analysis
- binary classification
- generalization ability
- support vectors
- classification using support vector machines
- data mining methods
- constructive induction
- decision rules
- kernel function
- naive bayes
- structured learning
- sparse kernel
- feature construction
- majority voting
- bayesian classifiers
- ensemble methods
- rule induction
- random forests
- random forest
- kernel methods
- cost sensitive learning
- imbalanced data
- cost sensitive
- feature extraction
- feature vectors
- knn
- machine learning algorithms
- neural network
- multi class classification
- learning algorithm
- decision tree algorithm
- classification algorithm
- reduced set
- base classifiers
- feature ranking