Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers

Henrik Linusson
  • Fonction : Auteur
  • PersonId : 992371
Ulf Johansson
  • Fonction : Auteur
  • PersonId : 992372
Henrik Boström
  • Fonction : Auteur
  • PersonId : 992373
Tuve Löfström
  • Fonction : Auteur
  • PersonId : 992374

Résumé

In the conformal prediction literature, it appears axiomatic that transductive conformal classifiers possess a higher predictive efficiency than inductive conformal classifiers, however, this depends on whether or not the nonconformity function tends to overfit misclassified test examples. With the conformal prediction framework’s increasing popularity, it thus becomes necessary to clarify the settings in which this claim holds true. In this paper, the efficiency of transductive conformal classifiers based on decision tree, random forest and support vector machine classification models is compared to the efficiency of corresponding inductive conformal classifiers. The results show that the efficiency of conformal classifiers based on standard decision trees or random forests is substantially improved when used in the inductive mode, while conformal classifiers based on support vector machines are more efficient in the transductive mode. In addition, an analysis is presented that discusses the effects of calibration set size on inductive conformal classifier efficiency.
Fichier principal
Vignette du fichier
978-3-662-44722-2_28_Chapter.pdf (1.24 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01391053 , version 1 (02-11-2016)

Licence

Paternité

Identifiants

Citer

Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström. Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.261-270, ⟨10.1007/978-3-662-44722-2_28⟩. ⟨hal-01391053⟩
43 Consultations
116 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More