Yiǧilmiş genelleme siniflandiricilarinin performans analizi

Translated title of the contribution: Performance analysis of stacked generalization classifiers

Mete Ozay, Fatoş Tünay Yarman Vural

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Stacked Generalization is a classification technique which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. In many applications, this technique, performs better than other classification schemas under some circumstances. However, in some applications, the performance of the technique goes astray, for the reasons that are not well-known. Even though it is used in several application domains up to now, it is not clear under which circumstances Stacked Generalization technique increases the performance. In this work, the states of the performance of Stacked Generalization technique is analyzed in terms of the performance parameters of the individual classifiers under the architecture. This work shows that the individual classifiers should learn the training set sharing the members of the set among themselves for the success of the Stacked Generalization architecture.

Translated title of the contributionPerformance analysis of stacked generalization classifiers
Original languageTurkish
Title of host publication2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU
DOIs
Publication statusPublished - 2008 Nov 26
Externally publishedYes
Event2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU - Aydin, Turkey
Duration: 2008 Apr 202008 Apr 22

Publication series

Name2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU

Conference

Conference
CountryTurkey
CityAydin
Period08/4/2008/4/22

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Communication

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