Boosting performance of gene mention tagging system by classifiers ensemble.
Lishuang LiJing SunDegen HuangPublished in: NLPKE (2010)
Keyphrases
- ensemble learning
- ensemble classifier
- weak learners
- weak classifiers
- randomized trees
- ensemble methods
- base classifiers
- majority voting
- strong classifier
- ensemble classification
- multiple classifier systems
- weighted voting
- multiple classifiers
- classifier ensemble
- accurate classifiers
- individual classifiers
- base learners
- decision stumps
- combining classifiers
- boosting algorithms
- decision trees
- training set
- generalization ability
- final classification
- linear classifiers
- ensemble pruning
- random forest
- feature selection
- naive bayes
- ensemble members
- gene expression
- training samples
- multi class
- classifier combination
- training data
- binary classification problems
- adaboost algorithm
- combining multiple
- random forests
- machine learning methods
- microarray
- imbalanced data
- negative correlation learning
- boosting framework
- fusion methods
- binary classifiers
- multi class classification
- multiclass classification
- gene expression data
- support vector
- prediction accuracy
- machine learning
- improving classification accuracy
- learning algorithm
- metadata
- knn
- support vector machine
- benchmark datasets
- loss function
- cost sensitive
- concept drift
- binary classification
- fusion method