A review on conditional random fields as a sequential classifier in machine learning

Dewi Yanti Liliana, T. Basaruddin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper we present a comprehensive review of a well-known sequential classifier in machine learning Conditional Random Fields (CRFs). CRFs is proposed to cope the limitation of both generative Hidden Markov Models (HMMs) and discriminative Maximum Entropy Markov Models (MEMMs) for solving the sequential classification problems. CRFs is widely used to accomplish the sequential classification which has a temporal dimension. On its way, CRFs has been improved both on the structural learning model as well as on the area of implementation. Those areas are varying from information extraction, image understanding, computer vision, behavioral analysis, natural language processing, bioinformatics, etc. This review provides a compact and informative summary of the major research on CRFs. We present a brief description about CRFs fundamental, CRFs roadmap, and CRFs related area of implementation from several literature papers on CRFs. The contribution of this paper is to explore the roadmap of CRFs research and potential prospect in developing CRFs to solve machine learning problems, particularly problems with sequential structures.

Original languageEnglish
Title of host publicationICECOS 2017 - Proceeding of 2017 International Conference on Electrical Engineering and Computer Science
Subtitle of host publicationSustaining the Cultural Heritage Toward the Smart Environment for Better Future
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages143-148
Number of pages6
ISBN (Electronic)9781479976751
DOIs
Publication statusPublished - 1 Jan 2017
Event2017 International Conference on Electrical Engineering and Computer Science, ICECOS 2017 - Palembang, Indonesia
Duration: 22 Aug 201723 Aug 2017

Publication series

NameICECOS 2017 - Proceeding of 2017 International Conference on Electrical Engineering and Computer Science: Sustaining the Cultural Heritage Toward the Smart Environment for Better Future

Conference

Conference2017 International Conference on Electrical Engineering and Computer Science, ICECOS 2017
CountryIndonesia
CityPalembang
Period22/08/1723/08/17

Keywords

  • conditional random fields
  • machine learning
  • sequential classifier
  • structured learning

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    Liliana, D. Y., & Basaruddin, T. (2017). A review on conditional random fields as a sequential classifier in machine learning. In ICECOS 2017 - Proceeding of 2017 International Conference on Electrical Engineering and Computer Science: Sustaining the Cultural Heritage Toward the Smart Environment for Better Future (pp. 143-148). [8167121] (ICECOS 2017 - Proceeding of 2017 International Conference on Electrical Engineering and Computer Science: Sustaining the Cultural Heritage Toward the Smart Environment for Better Future). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECOS.2017.8167121