Collective Multi-Label Classification

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%.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA440081

Entities

People

  • Andrew McCallum
  • Nadia Ghamrawi

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Air Force
  • Artificial Intelligence Computing
  • Biomedical Research
  • Classification
  • Data Sets
  • Errors
  • Gaussian Distributions
  • Graphs
  • Hard Copy
  • Learning
  • Machine Learning
  • Neural Networks
  • Probability
  • Standards
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks