Classification of FIB/SEM-tomography images for highly porous multiphase materials using random forest classifiers.
Markus OsenbergAndré HilgerMatthias NeumannAmalia WagnerNicole BohnJoachim R. BinderVolker SchmidtJohn BanhartIngo MankePublished in: CoRR (2022)
Keyphrases
- random forest
- decision trees
- feature set
- fold cross validation
- image classification
- ensemble classifier
- random forests
- machine learning algorithms
- extracted features
- classification accuracy
- input image
- decision tree learning algorithms
- classification algorithm
- support vector
- rotation forest
- class labels
- classification models
- multi label
- svm classifier
- feature selection
- image features
- cancer classification
- naive bayes
- classification method
- training samples
- classifier combination
- feature ranking
- base classifiers
- small number
- majority voting
- machine learning
- training set
- classification rate
- bayesian classifier
- similarity measure
- text classification
- feature extraction
- logistic regression
- ensemble learning
- supervised learning
- machine learning methods
- training data
- individual classifiers
- ensemble methods
- support vector machine svm