Apportioned margin approach for cost sensitive large margin classifiers

Lee Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the problem of cost sensitive multiclass classification, where we would like to increase the sensitivity of an important class at the expense of a less important one. We adopt an apportioned margin framework to address this problem, which enables an efficient margin shift between classes that share the same boundary. The decision boundary between all pairs of classes divides the margin between them in accordance with a given prioritization vector, which yields a tighter error bound for the important classes while also reducing the overall out-of-sample error. In addition to demonstrating an efficient implementation of our framework, we derive generalization bounds, demonstrate Fisher consistency, adapt the framework to Mercer’s kernel and to neural networks, and report promising empirical results on all accounts.

Original languageEnglish
Pages (from-to)1215-1235
Number of pages21
JournalAnnals of Mathematics and Artificial Intelligence
Volume89
Issue number12
DOIs
StatePublished - 1 Dec 2021

Keywords

  • Asymmetric cost
  • Linear classifiers
  • Multi-class classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Apportioned margin approach for cost sensitive large margin classifiers'. Together they form a unique fingerprint.

Cite this