On the performance of stacked generalization classifiers

Mete Ozay, Fatos Tunay Yarman Vural

研究成果: Conference contribution

12 被引用数 (Scopus)

抄録

Stacked Generalization (SG) is an ensemble learning 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 the individual classifiers. However, in some applications, the performance of the technique goes astray, for the reasons that are not well-known. In this work, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual classifiers under the architecture. This work shows that the success of the SG highly depends on how the individual classifiers share to learn the training set, rather than the performance of the individual classifiers. The experiments explore the learning mechanisms of SG to achieve the high performance. The relationship between the performance of the individual classifiers and that of SG is also investigated.

本文言語English
ホスト出版物のタイトルImage Analysis and Recognition - 5th International Conference, ICIAR 2008, Proceedings
ページ445-454
ページ数10
DOI
出版ステータスPublished - 2008 7月 28
イベント5th International Conference on Image Analysis and Recognition, ICIAR 2008 - Povoa de Varzim, Portugal
継続期間: 2008 6月 252008 6月 27

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5112 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other5th International Conference on Image Analysis and Recognition, ICIAR 2008
国/地域Portugal
CityPovoa de Varzim
Period08/6/2508/6/27

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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