Correlation-based feature ranking for online classification

Author

Osman, Hassab Elgawi

Author_Institution

Imaging Sci. & Eng. Lab., Tokyo Inst. of Technol., Tokyo, Japan

fYear

2009

fDate

11-14 Oct. 2009

Firstpage

3077

Lastpage

3082

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on

Conference_Location

San Antonio, TX

ISSN

1062-922X

Print_ISBN

978-1-4244-2793-2

Electronic_ISBN

1062-922X

Type

conf

DOI

10.1109/ICSMC.2009.5346141

Filename

5346141

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2568666