Work

An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification

Year: 2014

Type: conference-paper

Abstract: This paper studies multi-label classification problem in which data instances are associated with multiple, possibly high-dimensional, label vectors. This problem is especially challenging when labels are dependent and one cannot decompose the problem into a set of independent classification problems. To address the problem and properly represent label dependencies we propose and study a pairwise conditional random Field (CRF) model. We develop a new approach for learning the structure and parameters of the CRF from data. The approach maximizes the pseudo likelihood of observed labels and relies on the fast proximal gradient descend for learning the structure and limited memory BFGS for learning the parameters of the model. Empirical results on several datasets show that our approach outperforms several multi-label classification baselines, including recently published state-of-the-art methods.

Source: SIAM International Conference on Data Mining

DOI: 10.1137/1.9781611973440.113

Authors: Mahdi Pakdaman Naeini, Iyad Batal, Zitao Liu, Charmgil Hong, Milos Hauskrecht

Institutions: University of Pittsburgh, General Electric (United States)

Language: English

Topic: Text and Document Classification Technologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of General Medical Sciences, U.S. National Library of Medicine

Awards: Real-time detection of deviations in clinical care in ICU data streams, Using medical records repositories to improve the alert system design, Providing timely and effortless access to reliable health-related information for decision support and education