Entropy-Based Support Matrix Machine - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Entropy-Based Support Matrix Machine

Changming Zhu
  • Fonction : Auteur
  • PersonId : 1033373

Résumé

Support Vector Machine (SVM) cannot process imbalanced problem and matrix patterns. Thus, Fuzzy SVM (FSVM) is proposed to process imbalanced problem while Support Matrix Machine (SMM) is proposed to process matrix patterns. FSVM applies a fuzzy membership to each training pattern such that different patterns can make different contributions to the learning machine. However, how to evaluate fuzzy membership becomes the key point to FSVM. Although SMM can process matrix patterns, it still has no ability to process imbalanced problem. This paper adopts SMM as the basic and proposes an entropy-based support matrix machine for imbalanced data sets, i.e., ESMM. The contributions of ESMM are: (1) proposing an entropy-based fuzzy membership evaluation approach which enhances importance of certainty patterns, (2) guaranteeing importance of positive patterns and getting a more flexible decision surface. Experiments on real-world imbalanced data sets and matrix patterns validate the effectiveness of ESMM.
Fichier principal
Vignette du fichier
978-3-319-68121-4_21_Chapter.pdf (163.99 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01820906 , version 1 (22-06-2018)

Licence

Paternité

Identifiants

Citer

Changming Zhu. Entropy-Based Support Matrix Machine. 2nd International Conference on Intelligence Science (ICIS), Oct 2017, Shanghai, China. pp.200-211, ⟨10.1007/978-3-319-68121-4_21⟩. ⟨hal-01820906⟩
43 Consultations
80 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More