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2005, Methods in ecology., ISBN 0632053887, xii, 303
Insect Sampling in Forest Ecosystems highlights the problems faced by entomologists working in forest ecosystems. Insects play a major part in all aspects of... 
Forest insects | Sampling (Statistics) | Research | Forest surveys | Methodology | Ecological surveys | Insect populations
Book
2000, ISBN 9789067643146, 122
Book
Pattern Recognition, ISSN 0031-3203, 10/2019, Volume 94, pp. 13 - 24
We propose a reinforced quasi-random forest for classification task. Reinforcement is performed iteratively by adding new trees to the forest. Our method... 
Orthogonal decision trees | Importance of attributes | Random forest | Reinforcement learning | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 12/2017, Volume 89, pp. 389 - 403
Journal Article
Journal Article
ISPRS Journal of Photogrammetry and Remote Sensing, ISSN 0924-2716, 07/2015, Volume 105, pp. 38 - 53
Journal Article
Pattern Recognition Letters, ISSN 0167-8655, 2010, Volume 31, Issue 14, pp. 2225 - 2236
This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman... 
Regression | High dimensional data | Variable importance | Random forests | Classification | Variable selection | LINEAR-REGRESSION | GENE SELECTION | CANCER | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Usage | Analysis | Forests and forestry | Statistics | Statistics Theory | Mathematics
Journal Article
Advances in Space Research, ISSN 0273-1177, 06/2016, Volume 57, Issue 12, pp. 2464 - 2469
In this paper for the first time ensemble methods including Decision Tree, Bagging and Random Forest have been proposed in the field of earthquake precursors... 
TEC | Decision Tree | Anomaly detection | Bagging | Random Forest | NETWORK | GEOSCIENCES, MULTIDISCIPLINARY | SERIES | ASTRONOMY & ASTROPHYSICS | METEOROLOGY & ATMOSPHERIC SCIENCES | Earthquakes | Methods | Ionosphere | Earthquake prediction | Precursors | Time series | Decision trees | Total Electron Content | Anomalies
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 08/2015, Volume 37, Issue 8, pp. 1558 - 1570
Journal Article
The Annals of Statistics, ISSN 0090-5364, 8/2015, Volume 43, Issue 4, pp. 1716 - 1741
Journal Article
International Journal of Approximate Reasoning, ISSN 0888-613X, 2010, Volume 51, Issue 7, pp. 729 - 747
When individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple... 
Combination methods | Fuzzy sets | Random forest | Fuzzy decision tree | DECISION TREE | ENSEMBLES | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS | Fuzzy logic | Trees | Classifiers | Leaves | Forests | Classification | Fuzzy set theory | Fuzzy | Decision trees
Journal Article
Ecological Monographs, ISSN 0012-9615, 2/2011, Volume 81, Issue 1, pp. 25 - 41
Journal Article
Knowledge-Based Systems, ISSN 0950-7051, 01/2018, Volume 139, pp. 13 - 22
A Siamese Deep Forest (SDF) is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest... 
Deep learning | Quadratic optimization | Classification | Siamese | Random forest | Decision tree | Metric learning | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Neural networks | Training | Forests | Artificial intelligence | Optimization
Journal Article
Information Fusion, ISSN 1566-2535, 01/2016, Volume 27, pp. 111 - 125
A decision tree is a predictive model that recursively partitions the covariate’s space into subspaces such that each subspace constitutes a basis for a... 
Decision forest | Random forest | Decision tree | Classification tree | MINING DATA | ENSEMBLES | NETWORKS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS | PREDICTION | ACCURACY | TREES | COMPUTER SCIENCE, THEORY & METHODS | CLASSIFICATION PROBLEMS | ROTATION FOREST
Journal Article