Classification Using Rough Random Forest

  • Rajhans Gondane
  • V. Susheela Devi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)


The Rough random forest is a classification model based on rough set theory. The Rough random forest uses the concept of random forest and rough set theory in a single model. It combines a collection of decision trees for classification instead of depending on a single decision tree. It uses the concept of bagging and random subspace method to improve the performance of the classification model. In the rough random forest the reducts of each decision tree are chosen on the basis of boundary region condition. Each decision tree uses a different subset of patterns and features. The class label of patterns is obtained by combining the decisions of all the decision trees by majority voting. Results are reported on a number of benchmark datasets and compared with other techniques. Rough random forest is found to give better performance.


Decision Tree Random Forest Boundary Region Information Gain Ensemble Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Indian Institute of ScienceBangaloreIndia

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