An exploratory study of mono and multi-objective metaheuristics to ensemble of classifiers.
Antonino Feitosa NetoAnne M. P. CanutoPublished in: Appl. Intell. (2018)
Keyphrases
- multi objective
- particle swarm optimization
- ensemble learning
- ensemble classifier
- classifier ensemble
- multiple classifiers
- evolutionary algorithm
- ensemble pruning
- training data
- majority voting
- genetic algorithm
- multi objective optimization
- final classification
- optimization algorithm
- optimization problems
- individual classifiers
- weighted voting
- multi objective optimization problems
- class label noise
- training set
- combining classifiers
- randomized trees
- decision tree classifiers
- feature selection
- objective function
- multiple classifier systems
- ensemble methods
- majority vote
- mining concept drifting data streams
- bias variance decomposition
- accurate classifiers
- base classifiers
- simulated annealing
- decision trees
- support vector
- training samples
- ensemble classification
- neural network
- metaheuristic
- ensemble members
- random forest
- random forests
- class labels
- concept drifting data streams
- classifier combination
- learning algorithm
- diversity measures
- combinatorial optimization
- concept drift
- multiple objectives
- machine learning algorithms
- ant colony optimization
- trained classifiers
- conflicting objectives
- weak classifiers
- multi objective evolutionary algorithms
- imbalanced data
- weak learners
- combining multiple
- linear classifiers
- vehicle routing problem
- feature set
- optimal solution
- knn
- classification algorithm
- binary classification problems