Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Ensemble classification based on generalized additive models
Computational Statistics & Data Analysis
Pattern Classification Using Ensemble Methods
Pattern Classification Using Ensemble Methods
| Hi-index | 0.00 |
In this paper, a hybrid decision forest is constructed by double randomization of the original training set. In this decision forest, each individual base decision tree classifiers are incorporated with an additional classifier model, the Logitboosted decision stump. In the first randomization, the resamples to train the decision trees are extracted; in the second randomization, second set of resamples are generated from the out-of-bag samples of the first set of resamples. The boosted decision stumps are constructed on the second resamples. These extra resamples along with the resamples on which the base tree classifiers are trained, approximates the original training set. In this way we are utilizing the full training set to construct a hybrid decision forest with larger feature space. We have applied this hybrid decision forest in two real world applications; a) classifying credit scores, and b) short term extreme rainfall forecast. The performance of the hybrid decision forest in these two problems are compared with some well known machine learning methods. Overall results suggest that the new hybrid decision forest is capable of yielding commendable predictive performance.