LocalBoost: A Parallelizable Approach to Boosting Classifiers.
Carlos ValleRicardo ÑanculefHéctor AllendeClaudio MoragaPublished in: Neural Process. Lett. (2019)
Keyphrases
- ensemble learning
- randomized trees
- weak classifiers
- ensemble classifier
- boosting algorithms
- weak learners
- improving classification accuracy
- training data
- majority voting
- feature selection
- boosting framework
- strong classifier
- adaboost algorithm
- decision stumps
- decision trees
- accurate classifiers
- support vector
- multiple classifier systems
- ensemble classification
- discriminative classifiers
- weighted voting
- multiclass classification
- learning algorithm
- training samples
- ensemble methods
- multiple classifiers
- data sets
- training set
- multi class
- naive bayes
- machine learning algorithms
- bayesian classifiers
- classification systems
- classification algorithm
- boosted classifiers
- classification accuracy
- random forests
- loss function
- base classifiers
- classification trees
- linear classifiers
- classification method
- combining classifiers
- training examples
- classification models
- cost sensitive
- svm classifier
- supervised classification
- individual classifiers
- classifier combination