A Weight-adjusting Approach on an Ensemble of Classifiers for Time Series Forecasting.
Lin LiChun-Kit NganPublished in: ICISDM (2019)
Keyphrases
- ensemble learning
- ensemble classifier
- multiple classifiers
- classifier ensemble
- ensemble pruning
- training data
- majority voting
- training set
- ensemble methods
- decision trees
- randomized trees
- final classification
- multiple classifier systems
- combining classifiers
- individual classifiers
- support vector
- weighted voting
- weak classifiers
- majority vote
- decision tree classifiers
- feature weights
- ensemble classification
- artificial neural networks
- accurate classifiers
- base classifiers
- linear classifiers
- random forest
- feature selection
- classification algorithm
- ensemble members
- publicly available data sets
- classifier fusion
- feature set
- classifier combination
- one class support vector machines
- mining concept drifting data streams
- learning algorithm
- pruning method
- trained classifiers
- concept drifting data streams
- machine learning algorithms
- neural network
- imbalanced data
- svm classifier
- weight vector
- feature ranking
- binary classification problems
- diversity measures
- training examples
- naive bayes
- multi class
- binary classifiers
- random forests
- fusion methods
- class labels
- training samples
- support vector machine
- bias variance decomposition
- feature extraction