Title :
Correlation-based feature ranking for online classification
Author :
Osman, Hassab Elgawi
Author_Institution :
Imaging Sci. & Eng. Lab., Tokyo Inst. of Technol., Tokyo, Japan
Abstract :
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.
Keywords :
learning (artificial intelligence); pattern classification; correlation-based feature ranking; online classification; online training mode; random forest algorithm; Chromium; Cybernetics; Input variables; Machine learning; Machine learning algorithms; Radio frequency; Space exploration; Support vector machine classification; Support vector machines; USA Councils; NIPS 2003; ensemble learning; feature selection; on-line learning; random forests;
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2009.5346141
