Forecasting Corporate Bankruptcy with an Ensemble of Classifiers.
Despina DeligianniSotiris B. KotsiantisPublished in: SETN (2012)
Keyphrases
- ensemble learning
- classifier ensemble
- multiple classifiers
- ensemble pruning
- ensemble classifier
- training data
- majority voting
- training set
- final classification
- randomized trees
- feature selection
- combining classifiers
- individual classifiers
- weighted voting
- multiple classifier systems
- mining concept drifting data streams
- decision tree classifiers
- class label noise
- majority vote
- ensemble methods
- weak learners
- short term
- case study
- publicly available data sets
- ensemble classification
- neural network
- concept drifting data streams
- random forests
- decision trees
- support vector
- machine learning algorithms
- one class support vector machines
- diversity measures
- ensemble members
- accurate classifiers
- imbalanced data
- support vector regression
- classification algorithm
- weak classifiers
- base classifiers
- trained classifiers
- machine learning
- feature set
- knowledge management
- machine learning methods
- binary classification problems
- binary classifiers
- classification models
- random forest
- rule induction algorithm
- bankruptcy prediction
- pruning algorithm
- training samples
- concept drift