An improved multi-label classification based on label ranking and delicate boundary SVM

Benhui Chen, Weifeng Gu, Jinglu Hu

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

In this paper, an improved multi-label classification is proposed based on label ranking and delicate decision boundary SVM. Firstly, an improved probabilistic SVM with delicate decision boundary is used as the scoring method to obtain a proper label rank. It can improve the probabilistic label rank by introducing the information of overlapped training samples into learning procedure. Secondly, a threshold selection related with input instance and label rank is proposed to decide the classification results. It can estimate an appropriate threshold for each testing instance according to the characteristics of instance and label rank. Experimental results on four multi-label benchmark datasets show that the proposed method improves the performance of classification efficiently, compared with binary SVM method and some existing well-known methods.

本文言語English
ホスト出版物のタイトル2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOI
出版ステータスPublished - 2010 12 1
イベント2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
継続期間: 2010 7 182010 7 23

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CountrySpain
CityBarcelona
Period10/7/1810/7/23

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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