Local Grading of Learners

Author

Kotsiantis, Sotiris

Author_Institution

Dept. of Comput. Sci. & Technol., Peloponnese Univ., Peloponnese

fYear

2008

fDate

28-30 Aug. 2008

Firstpage

209

Lastpage

213

Abstract

We propose a technique of localized grading of weak classifiers. This technique identifies local regions having similar characteristics and then uses grading of weak classifiers to describe the relationship between the data characteristics and the target class. Our experiment for several UCI datasets shows that the proposed combining method outperforms other combining methods we tried as well as any base classifier.

Keywords

pattern classification; UCI datasets; data characteristics; local learning; localized grading; weak classifiers; Bayesian methods; Boosting; Computer science; Decision trees; Informatics; Learning systems; Machine learning; Machine learning algorithms; Testing; Voting; classification; ensemble of classifiers; supervised machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics, 2008. PCI '08. Panhellenic Conference on

Conference_Location

Samos

Print_ISBN

978-0-7695-3323-0

Type

conf

DOI

10.1109/PCI.2008.16

Filename

4621564

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2803576