Variable Ranking for Online Ensemble Learning - Osman, Hassab Elgawi (Journal of advanced computational intelligence and intelligent informatics, 2009)
In proposing, incremental feature selection based on correlation ranking (CR) for classification problems, we develop on-line training using the random forests (RF) algorithm, then evaluate the performance of the combination based on an NIPS 2003 Feature Selection Challenge dataset. Results show that our approach achieves performance comparable to others batch learning algorithms, including RF.