Accession Number:

ADA440081

Title:

Collective Multi-Label Classification

Descriptive Note:

Corporate Author:

MASSACHUSETTS UNIV AMHERST

Personal Author(s):

Report Date:

2005-01-01

Pagination or Media Count:

8.0

Abstract:

Common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. Because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent. However, in many domains labels are highly interdependent. This paper explores multi-label conditional random field CRF classification models that directly parameterize label co-occurrences in multi-label classification. Experiments show that the models outperform their single-label counterparts on standard text corpora. Even when multi-labels are sparse, the models improve subset classification error by as much as 40.

Subject Categories:

  • Operations Research
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE