Correlation-based feature ranking for online classification
Hassab Elgawi Osman · 2009
The contribution of this paper is two-fold. First, incremental feature selection based on correlation ranking (CR) is proposed for classification problems. Second, we develop online training mode using the random forests (RF) algorithm, then evaluate the performance of the combination based on the NIPS 2003 Feature Selection Challenge dataset. Results show that our approach achieves performance comparable to others batch learning algorithms, including RF.