We propose a classifier ensemble called RPC based on principles of rotation forest using random projections. Random projections project the original high dimensional data into lower dimensions while preserving the dataset's geometrical structure reducing classifier's complexity. Random projections are also an efficient dimensionality reduction tool, removing noisy features from dataset and representing the information using only small number of features. Training set for RPC is created by applying random projection on random subsets of the feature set. The randomness of random projection coupled with random sampling adds diversity to RPC. Initial evaluation using datasets from UCI machine learning repository shows that RPC performs equally well or better than Random Forest, Bagging and AdaBoost. We demonstrate that using dimensionality reduction with RPC we can dramatically reduce datasets dimensions without any loss in classification accuracy and significantly enhance computational performance. Finally, we experiment building RPC with different base learners.
['Lovedeep Gondara']
RPC: An Efficient Classifier Ensemble Using Random Projections