Prediction approach for ising model estimation

Jinyu Li, Yu Pan, Hongfeng Yu, Qi Zhang

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

Abstract

We consider the graph estimation for Ising model from observed binary data. Popular approaches in the literature are largely penalized sparse selection procedures that depend on tuning parameters to be selected. The output of such procedures is usually one single sparse graph without any ranking information of the individual edges. In scientific practice, however, it is more desirable to be able to rank all potential edges based on their statistical significance, and select the sparse graph by thresholding. In this paper, we propose a novel PRediction Approach for Ising Model Estimation (PRAIME). The proposed framework reformulates Ising model estimation as the prediction of the observed data, and provides an estimate and a statistical significance measure of the Ising model parameter for each node pair using only the predicted values. Thus it enables the ranking all potential edges and the flexible sparse graph selection by thresholding, and allows the researchers to use the predictive algorithm of their choice. We implemented PRAIME using random forest, illustrated the advantage of PRAIME over the penalized sparse selection approaches in accuracy and flexibility using synthetic data, and applied it to a congress co-sponsorship dataset.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
EditorsPanagiotis Papapetrou, Xueqi Cheng, Qing He
PublisherIEEE Computer Society
Pages703-710
Number of pages8
ISBN (Electronic)9781728146034
DOIs
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2019-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
CountryChina
CityBeijing
Period11/8/1911/11/19

Keywords

  • Graphical model
  • Ising model
  • Random forest

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    Li, J., Pan, Y., Yu, H., & Zhang, Q. (2019). Prediction approach for ising model estimation. In P. Papapetrou, X. Cheng, & Q. He (Eds.), Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 (pp. 703-710). [8955520] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2019.00106