Software package for regression algorithms based on Gaussian Conditional Random Fields

Tijana Markovic; Vladan Devedzic; Fang Zhou; Zoran Obradovic

doi:10.1109/ICMLA55696.2022.00184

Software package for regression algorithms based on Gaussian Conditional Random Fields

Tijana Markovic^*, Vladan Devedzic, Fang Zhou, Zoran Obradovic

^*Corresponding author for this work

School of Data Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The Gaussian Conditional Random Fields (GCRF) algorithm and its extensions are used for machine learning regression problems in which the attributes of objects and the correlation between objects should be considered when making predictions. These algorithms can be applied in different domains where problems can be seen as graphs, but their implementation requires complex calculations and good programming skills. This paper presents an open source software package that includes a tool with graphical user interface (GCRFs tool) and Java library (GCRFs library). GCRFs tool is software that integrates various GCRF-based algorithms and supports training and testing of those algorithms on real-world datasets. The main goal of GCRFs tool is to provide a straightforward and user-friendly graphical user interface that will simplify the use of GCRF-based algorithms. GCRFs Java library contains basic classes for GCRF concepts and can be used by researchers who have experience in Java programming. Also, this paper presents the results of a pilot usability evaluation of the GCRFs tool, where the software was evaluated with expert and non-expert users. This evaluation gave us detailed insight into the experiences and opinions of the users and helped us outline priorities for future development.

Original language	English
Title of host publication	Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022
Editors	M. Arif Wani, Mehmed Kantardzic, Vasile Palade, Daniel Neagu, Longzhi Yang, Kit-Yan Chan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1121-1128
Number of pages	8
ISBN (Electronic)	9781665462839
DOIs	https://doi.org/10.1109/ICMLA55696.2022.00184
State	Published - 2022
Event	21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 - Nassau, Bahamas Duration: 12 Dec 2022 → 14 Dec 2022

Publication series

Name	Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022

Conference

Conference	21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022
Country/Territory	Bahamas
City	Nassau
Period	12/12/22 → 14/12/22

Keywords

Gaussian Conditional Random Fields
Graphs
Library
Regression Algorithms
Software

Access to Document

10.1109/ICMLA55696.2022.00184

Cite this

Markovic, T., Devedzic, V., Zhou, F., & Obradovic, Z. (2022). Software package for regression algorithms based on Gaussian Conditional Random Fields. In M. A. Wani, M. Kantardzic, V. Palade, D. Neagu, L. Yang, & K.-Y. Chan (Eds.), Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 (pp. 1121-1128). (Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA55696.2022.00184

Markovic, Tijana ; Devedzic, Vladan ; Zhou, Fang et al. / Software package for regression algorithms based on Gaussian Conditional Random Fields. Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022. editor / M. Arif Wani ; Mehmed Kantardzic ; Vasile Palade ; Daniel Neagu ; Longzhi Yang ; Kit-Yan Chan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1121-1128 (Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022).

@inproceedings{5e1f2b3005334b0cb4d2d329254cc3bb,

title = "Software package for regression algorithms based on Gaussian Conditional Random Fields",

abstract = "The Gaussian Conditional Random Fields (GCRF) algorithm and its extensions are used for machine learning regression problems in which the attributes of objects and the correlation between objects should be considered when making predictions. These algorithms can be applied in different domains where problems can be seen as graphs, but their implementation requires complex calculations and good programming skills. This paper presents an open source software package that includes a tool with graphical user interface (GCRFs tool) and Java library (GCRFs library). GCRFs tool is software that integrates various GCRF-based algorithms and supports training and testing of those algorithms on real-world datasets. The main goal of GCRFs tool is to provide a straightforward and user-friendly graphical user interface that will simplify the use of GCRF-based algorithms. GCRFs Java library contains basic classes for GCRF concepts and can be used by researchers who have experience in Java programming. Also, this paper presents the results of a pilot usability evaluation of the GCRFs tool, where the software was evaluated with expert and non-expert users. This evaluation gave us detailed insight into the experiences and opinions of the users and helped us outline priorities for future development.",

keywords = "Gaussian Conditional Random Fields, Graphs, Library, Regression Algorithms, Software",

author = "Tijana Markovic and Vladan Devedzic and Fang Zhou and Zoran Obradovic",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 ; Conference date: 12-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/ICMLA55696.2022.00184",

language = "英语",

series = "Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1121--1128",

editor = "Wani, \{M. Arif\} and Mehmed Kantardzic and Vasile Palade and Daniel Neagu and Longzhi Yang and Kit-Yan Chan",

booktitle = "Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022",

address = "美国",

}

Markovic, T, Devedzic, V, Zhou, F & Obradovic, Z 2022, Software package for regression algorithms based on Gaussian Conditional Random Fields. in MA Wani, M Kantardzic, V Palade, D Neagu, L Yang & K-Y Chan (eds), Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022. Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022, Institute of Electrical and Electronics Engineers Inc., pp. 1121-1128, 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022, Nassau, Bahamas, 12/12/22. https://doi.org/10.1109/ICMLA55696.2022.00184

Software package for regression algorithms based on Gaussian Conditional Random Fields. / Markovic, Tijana; Devedzic, Vladan; Zhou, Fang et al.
Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022. ed. / M. Arif Wani; Mehmed Kantardzic; Vasile Palade; Daniel Neagu; Longzhi Yang; Kit-Yan Chan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1121-1128 (Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Software package for regression algorithms based on Gaussian Conditional Random Fields

AU - Markovic, Tijana

AU - Devedzic, Vladan

AU - Zhou, Fang

AU - Obradovic, Zoran

PY - 2022

Y1 - 2022

N2 - The Gaussian Conditional Random Fields (GCRF) algorithm and its extensions are used for machine learning regression problems in which the attributes of objects and the correlation between objects should be considered when making predictions. These algorithms can be applied in different domains where problems can be seen as graphs, but their implementation requires complex calculations and good programming skills. This paper presents an open source software package that includes a tool with graphical user interface (GCRFs tool) and Java library (GCRFs library). GCRFs tool is software that integrates various GCRF-based algorithms and supports training and testing of those algorithms on real-world datasets. The main goal of GCRFs tool is to provide a straightforward and user-friendly graphical user interface that will simplify the use of GCRF-based algorithms. GCRFs Java library contains basic classes for GCRF concepts and can be used by researchers who have experience in Java programming. Also, this paper presents the results of a pilot usability evaluation of the GCRFs tool, where the software was evaluated with expert and non-expert users. This evaluation gave us detailed insight into the experiences and opinions of the users and helped us outline priorities for future development.

AB - The Gaussian Conditional Random Fields (GCRF) algorithm and its extensions are used for machine learning regression problems in which the attributes of objects and the correlation between objects should be considered when making predictions. These algorithms can be applied in different domains where problems can be seen as graphs, but their implementation requires complex calculations and good programming skills. This paper presents an open source software package that includes a tool with graphical user interface (GCRFs tool) and Java library (GCRFs library). GCRFs tool is software that integrates various GCRF-based algorithms and supports training and testing of those algorithms on real-world datasets. The main goal of GCRFs tool is to provide a straightforward and user-friendly graphical user interface that will simplify the use of GCRF-based algorithms. GCRFs Java library contains basic classes for GCRF concepts and can be used by researchers who have experience in Java programming. Also, this paper presents the results of a pilot usability evaluation of the GCRFs tool, where the software was evaluated with expert and non-expert users. This evaluation gave us detailed insight into the experiences and opinions of the users and helped us outline priorities for future development.

KW - Gaussian Conditional Random Fields

KW - Graphs

KW - Library

KW - Regression Algorithms

KW - Software

UR - https://www.scopus.com/pages/publications/85152213930

U2 - 10.1109/ICMLA55696.2022.00184

DO - 10.1109/ICMLA55696.2022.00184

M3 - 会议稿件

AN - SCOPUS:85152213930

T3 - Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022

SP - 1121

EP - 1128

BT - Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022

A2 - Wani, M. Arif

A2 - Kantardzic, Mehmed

A2 - Palade, Vasile

A2 - Neagu, Daniel

A2 - Yang, Longzhi

A2 - Chan, Kit-Yan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022

Y2 - 12 December 2022 through 14 December 2022

ER -

Markovic T, Devedzic V, Zhou F, Obradovic Z. Software package for regression algorithms based on Gaussian Conditional Random Fields. In Wani MA, Kantardzic M, Palade V, Neagu D, Yang L, Chan KY, editors, Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1121-1128. (Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022). doi: 10.1109/ICMLA55696.2022.00184

Software package for regression algorithms based on Gaussian Conditional Random Fields

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this