A Comparative Analysis of Machine Learning Classifiers and Ensemble Techniques in Financial Distress Prediction.
Meenu SreedharanAhmed M. KhedrMagdi El BannanyPublished in: SSD (2020)
Keyphrases
- machine learning
- ensemble learning
- multiple classifier systems
- machine learning methods
- machine learning algorithms
- ensemble classifier
- feature selection
- decision trees
- ensemble methods
- multiple classifiers
- classifier ensemble
- training data
- machine learning approaches
- ensemble pruning
- learning algorithm
- supervised classification
- rule induction algorithm
- feature ranking
- training set
- majority voting
- randomized trees
- combining classifiers
- random forests
- decision tree classifiers
- ensemble classification
- majority vote
- increase classification accuracy
- accurate classifiers
- final classification
- ensemble members
- class label noise
- learning tasks
- concept drifting data streams
- support vector
- imbalanced data
- individual classifiers
- base classifiers
- training samples
- trained classifiers
- random forest
- pattern recognition
- support vector machine
- weighted voting
- natural language processing
- mining concept drifting data streams
- weak learners
- meta learning
- semi supervised learning
- feature set
- naive bayes
- supervised learning
- classification accuracy
- learning classifier systems
- neural network
- classifier combination
- pruning algorithm
- binary classification problems
- classification algorithm
- classification models
- one class support vector machines
- linear classifiers
- publicly available data sets
- diversity measures
- weak classifiers
- svm classifier
- class labels
- model selection
- data mining