Ensemble of heterogeneous classifiers applied to lithofacies classification using logs from different wells.
Joacir Marques de OliveiraEulanda Miranda dos SantosJosé Reginaldo Hughes CarvalhoLeyne Abuim de Vasconcelos MarquesPublished in: IJCNN (2013)
Keyphrases
- final classification
- multiple classifiers
- ensemble classifier
- training set
- majority voting
- classification algorithm
- classification systems
- support vector
- classifier ensemble
- decision trees
- combining classifiers
- svm classifier
- individual classifiers
- decision tree classifiers
- classification method
- multiple classifier systems
- classification accuracy
- feature selection
- training data
- class labels
- binary classifiers
- classification rate
- classifier combination
- classification models
- multi class
- classification decisions
- supervised classification
- machine learning methods
- ensemble classification
- training samples
- probabilistic classifiers
- supervised learning
- decision boundary
- improves the classification accuracy
- wrapper feature selection
- accurate classifiers
- multiclass classification
- weak learners
- ensemble methods
- k nearest neighbour
- ensemble learning
- optimum path forest
- text classification
- neural network
- machine learning
- randomized trees
- binary classification problems
- classification process
- machine learning algorithms
- image classification
- feature set
- support vector machine
- knn
- accurate classification
- imbalanced data
- feature ranking
- combining multiple
- random forests
- base classifiers
- rule based classifier
- feature vectors