Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A structural/statistical feature based vector for handwritten character recognition
Pattern Recognition Letters
Machine Learning
Limiting the Number of Trees in Random Forests
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
Machine Learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A two-stage outlier rejection strategy for numerical field extraction in handwritten documents
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Using Random Forests for Handwritten Digit Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
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Random Linear Oracle (RLO) ensembles of Naive Bayes classifiers show excellent performance [12]. In this paper, we investigate the reasons for the success of RLO ensembles. Our study suggests that the decomposition of most of the classes of the dataset into two subclasses for each class is the reason for the success of the RLO method. Our study leads to the development of a new output manipulation based ensemble method; Random Subclasses (RS). In the proposed method, we create new subclasses from each subset of data points that belongs to the same class using RLO framework and consider each subclass as a class of its own. The comparative study suggests that RS is similar to RLO method, whereas RS is statistically better than or similar to Bagging and AdaBoost.M1 for most of the datasets. The similar performance of RLO and RS suggest that the creation of local structures (subclasses) is the main reason for the success of RLO. The another conclusion of this study is that RLO is more useful for classifiers (linear classifiers etc.) that have limited flexibility in their class boundaries. These classifiers can not learn complex class boundaries. Creating subclasses makes new, easier to learn, class boundaries.