A hierarchical SVM based multiclass classification by using similarity clustering

Chao Dong, Bo Zhou, Takayuki Furuzuki

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

This paper presents a new strategy to build multi tree hierarchical structure SVM which can get a more efficient and accuracy classification model for multiclass problems. Base on the theory of Binary Tree SVM (BTS), we proposed an improvement algorithm which extend binary tree structure to a multi tree structure, In the multi tree hierarchical structure, similarity clustering method was proposed to cluster classes to groups in each non-leaf node. In order to get a multi node division, one-against-all (OAA) was applied to train those groups rather than classes. The proposed method can avoid data imbalanced problem occurred in OAA, also the classification area of classifier in the upper layer is larger than classifier in lower layer. Compared with other several well-known methods, experiments on many data sets demonstrate that our method can reduce the number of classifiers in the testing phase and get a higher accuracy.

本文言語English
ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
出版社Institute of Electrical and Electronics Engineers Inc.
2015-September
ISBN(印刷版)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOI
出版ステータスPublished - 2015 9 28
イベントInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
継続期間: 2015 7 122015 7 17

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period15/7/1215/7/17

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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