Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction

Zaineb Chelly Dagdia; Christine Zarges; Benjamin Schannes; Martin Micalef; Lino Galiana; Benoît Rolland; Olivier de Fresnoye; Mehdi Benchoufi

doi:10.1007/978-3-030-10997-4_27

Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction

Zaineb Chelly Dagdia, Christine Zarges, Benjamin Schannes, Martin Micalef, Lino Galiana, Benoît Rolland, Olivier de Fresnoye, Mehdi Benchoufi

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

3 Citations (SciVal)

246 Downloads (Pure)

Abstract

A big challenge in epidemiology is to perform data pre-processing, specifically feature selection, on a large scale data and high dimensional feature set. In this paper, this challenge is tackled by using a recently established distributed and scalable version of Rough Set Theory (RST). It considers epidemiological data that has been collected from three international institutions for the purpose of cancer incidence prediction. The concrete data set used aggregates about 5495 risk factors (features), spanning 32 years and 38 countries. Detailed experiments demonstrate that RST is relevant to real world big data applications as it can offer insights about the selected risk factors, speed up the learning process, assure the performance of the cancer incidence prediction model without a significant information loss, and simplify the learned model for epidemiologists. Code related to this paper is available at: https://github.com/zeinebchelly/Sp-RST.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	ECML PKDD 20-18
Editors	Ulf Brefeld, Edward Curry, Elizabeth Daly, Brian MacNamee, Alice Marascu, Fabio Pinelli, Michele Berlingerio, Neil Hurley
Publisher	Springer Nature
Pages	440-455
Number of pages	16
ISBN (Electronic)	978-3-030-10997-4
ISBN (Print)	978-3-030-10996-7
DOIs	https://doi.org/10.1007/978-3-030-10997-4_27
Publication status	Published - 18 Jan 2019
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Croke Park Conference Centre, Dublin, Ireland Duration: 10 Sept 2018 → 14 Sept 2018 http://www.ecmlpkdd2018.org

Publication series

Name	Lecture Notes in Computer Science
Volume	11053

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated title	ECML-PKDD
Country/Territory	Ireland
City	Dublin
Period	10 Sept 2018 → 14 Sept 2018
Internet address	http://www.ecmlpkdd2018.org

Keywords

Big Data
Rough Set Theory
Feature Selection
Epidemiology
Cancer Incidence Prediction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-10997-4_27

ECML2018_EpidemiumAccepted author manuscript, 1.11 MBLicence: CC BY-NC-ND

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Cite this

Chelly Dagdia, Z., Zarges, C., Schannes, B., Micalef, M., Galiana, L., Rolland, B., de Fresnoye, O., & Benchoufi, M. (2019). Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, & N. Hurley (Eds.), Machine Learning and Knowledge Discovery in Databases: ECML PKDD 20-18 (pp. 440-455). (Lecture Notes in Computer Science ; Vol. 11053). Springer Nature. https://doi.org/10.1007/978-3-030-10997-4_27

Chelly Dagdia, Zaineb ; Zarges, Christine ; Schannes, Benjamin et al. / Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction. Machine Learning and Knowledge Discovery in Databases: ECML PKDD 20-18. editor / Ulf Brefeld ; Edward Curry ; Elizabeth Daly ; Brian MacNamee ; Alice Marascu ; Fabio Pinelli ; Michele Berlingerio ; Neil Hurley. Springer Nature, 2019. pp. 440-455 (Lecture Notes in Computer Science ).

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title = "Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction",

abstract = "A big challenge in epidemiology is to perform data pre-processing, specifically feature selection, on a large scale data and high dimensional feature set. In this paper, this challenge is tackled by using a recently established distributed and scalable version of Rough Set Theory (RST). It considers epidemiological data that has been collected from three international institutions for the purpose of cancer incidence prediction. The concrete data set used aggregates about 5495 risk factors (features), spanning 32 years and 38 countries. Detailed experiments demonstrate that RST is relevant to real world big data applications as it can offer insights about the selected risk factors, speed up the learning process, assure the performance of the cancer incidence prediction model without a significant information loss, and simplify the learned model for epidemiologists. Code related to this paper is available at: https://github.com/zeinebchelly/Sp-RST.",

keywords = "Big Data, Rough Set Theory, Feature Selection, Epidemiology, Cancer Incidence Prediction",

author = "{Chelly Dagdia}, Zaineb and Christine Zarges and Benjamin Schannes and Martin Micalef and Lino Galiana and Beno{\^i}t Rolland and {de Fresnoye}, Olivier and Mehdi Benchoufi",

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language = "English",

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Chelly Dagdia, Z, Zarges, C, Schannes, B, Micalef, M, Galiana, L, Rolland, B, de Fresnoye, O & Benchoufi, M 2019, Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction. in U Brefeld, E Curry, E Daly, B MacNamee, A Marascu, F Pinelli, M Berlingerio & N Hurley (eds), Machine Learning and Knowledge Discovery in Databases: ECML PKDD 20-18. Lecture Notes in Computer Science , vol. 11053, Springer Nature, pp. 440-455, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Dublin, Ireland, 10 Sept 2018. https://doi.org/10.1007/978-3-030-10997-4_27

Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction. / Chelly Dagdia, Zaineb; Zarges, Christine; Schannes, Benjamin et al.

Machine Learning and Knowledge Discovery in Databases: ECML PKDD 20-18. ed. / Ulf Brefeld; Edward Curry; Elizabeth Daly; Brian MacNamee; Alice Marascu; Fabio Pinelli; Michele Berlingerio; Neil Hurley. Springer Nature, 2019. p. 440-455 (Lecture Notes in Computer Science ; Vol. 11053).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

TY - GEN

T1 - Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction

AU - Chelly Dagdia, Zaineb

AU - Zarges, Christine

AU - Schannes, Benjamin

AU - Micalef, Martin

AU - Galiana, Lino

AU - Rolland, Benoît

AU - de Fresnoye, Olivier

AU - Benchoufi, Mehdi

PY - 2019/1/18

Y1 - 2019/1/18

N2 - A big challenge in epidemiology is to perform data pre-processing, specifically feature selection, on a large scale data and high dimensional feature set. In this paper, this challenge is tackled by using a recently established distributed and scalable version of Rough Set Theory (RST). It considers epidemiological data that has been collected from three international institutions for the purpose of cancer incidence prediction. The concrete data set used aggregates about 5495 risk factors (features), spanning 32 years and 38 countries. Detailed experiments demonstrate that RST is relevant to real world big data applications as it can offer insights about the selected risk factors, speed up the learning process, assure the performance of the cancer incidence prediction model without a significant information loss, and simplify the learned model for epidemiologists. Code related to this paper is available at: https://github.com/zeinebchelly/Sp-RST.

AB - A big challenge in epidemiology is to perform data pre-processing, specifically feature selection, on a large scale data and high dimensional feature set. In this paper, this challenge is tackled by using a recently established distributed and scalable version of Rough Set Theory (RST). It considers epidemiological data that has been collected from three international institutions for the purpose of cancer incidence prediction. The concrete data set used aggregates about 5495 risk factors (features), spanning 32 years and 38 countries. Detailed experiments demonstrate that RST is relevant to real world big data applications as it can offer insights about the selected risk factors, speed up the learning process, assure the performance of the cancer incidence prediction model without a significant information loss, and simplify the learned model for epidemiologists. Code related to this paper is available at: https://github.com/zeinebchelly/Sp-RST.

KW - Big Data

KW - Rough Set Theory

KW - Feature Selection

KW - Epidemiology

KW - Cancer Incidence Prediction

UR - https://github.com/zeinebchelly/Sp-RST.

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DO - 10.1007/978-3-030-10997-4_27

M3 - Conference Proceeding (Non-Journal item)

SN - 978-3-030-10996-7

T3 - Lecture Notes in Computer Science

SP - 440

EP - 455

BT - Machine Learning and Knowledge Discovery in Databases

A2 - Brefeld, Ulf

A2 - Curry, Edward

A2 - Daly, Elizabeth

A2 - MacNamee, Brian

A2 - Marascu, Alice

A2 - Pinelli, Fabio

A2 - Berlingerio, Michele

A2 - Hurley, Neil

PB - Springer Nature

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Y2 - 10 September 2018 through 14 September 2018

ER -

Chelly Dagdia Z, Zarges C, Schannes B, Micalef M, Galiana L, Rolland B et al. Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction. In Brefeld U, Curry E, Daly E, MacNamee B, Marascu A, Pinelli F, Berlingerio M, Hurley N, editors, Machine Learning and Knowledge Discovery in Databases: ECML PKDD 20-18. Springer Nature. 2019. p. 440-455. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-030-10997-4_27

Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Permanent link

Other files and links

Fingerprint

Optimised Framework based on Rough Set Theory for Big Data Pre-processing in Certain and Imprecise Contexts - RoSTBiDFramework

Supercomputing Wales

Cite this

Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Permanent link

Other files and links

Fingerprint

Projects

Optimised Framework based on Rough Set Theory for Big Data Pre-processing in Certain and Imprecise Contexts - RoSTBiDFramework

Equipment

Supercomputing Wales

Cite this