Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers.
Uwe KnauerCornelius Styp von RekowskiMarianne StecklinaTilman KrokotschTuan Pham MinhViola HauffeDavid KiliasIna EhrhardtHerbert SagischewskiSergej ChmaraUdo SeiffertPublished in: Remote. Sens. (2019)
Keyphrases
- random forest
- convolutional neural network
- decision trees
- ensemble classifier
- random subspace
- rotation forest
- ensemble methods
- fold cross validation
- feature set
- ensemble learning
- random forests
- face detection
- feature ranking
- imbalanced data
- majority voting
- base classifiers
- cancer classification
- machine learning methods
- individual classifiers
- classifier ensemble
- classification models
- decision tree learning algorithms
- naive bayes
- feature selection
- decision tree learning algorithm
- classification accuracy
- multiple classifier systems
- support vector
- machine learning algorithms
- generalization ability
- feature vectors
- training set
- classification algorithm
- prediction accuracy
- feature space
- training data
- subspace methods
- data sets
- classifier fusion
- face recognition
- text classification
- benchmark datasets
- class labels
- image features
- markov random field
- training samples
- feature subset
- logistic regression
- multi label
- svm classifier