Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets: A comparative study

Pedro Lopez-Garcia; Antonio D. Masegosa; Enrique Onieva; Eneko Osaba

doi:10.1007/978-3-319-91641-5_16

Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets: A comparative study

Pedro Lopez-Garcia^*
, Antonio D. Masegosa
, Enrique Onieva
, Eneko Osaba

^*Corresponding author for this work

University of Deusto

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

2 Citations (Scopus)

Abstract

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets.

Original language	English
Title of host publication	Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings
Editors	Nouredine Melab, Peter Korosec, El-Ghazali Talbi
Publisher	Springer Verlag
Pages	185-196
Number of pages	12
ISBN (Print)	9783319916408
DOIs	https://doi.org/10.1007/978-3-319-91641-5_16
Publication status	Published - 2018
Externally published	Yes
Event	8th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2018 - Paris, France Duration: 16 May 2018 → 18 May 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10835 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2018
Country/Territory	France
City	Paris
Period	16/05/18 → 18/05/18

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

Classification
Ensemble techniques
Fuzzy techniques
Imbalanced data
Intelligent transportation systems
Soft computing techniques

Access to Document

10.1007/978-3-319-91641-5_16

Cite this

Lopez-Garcia, P., Masegosa, A. D., Onieva, E., & Osaba, E. (2018). Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets: A comparative study. In N. Melab, P. Korosec, & E.-G. Talbi (Eds.), Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings (pp. 185-196). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10835 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-91641-5_16

Lopez-Garcia, Pedro ; Masegosa, Antonio D. ; Onieva, Enrique et al. / Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets : A comparative study. Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings. editor / Nouredine Melab ; Peter Korosec ; El-Ghazali Talbi. Springer Verlag, 2018. pp. 185-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets: A comparative study",

abstract = "Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets.",

keywords = "Classification, Ensemble techniques, Fuzzy techniques, Imbalanced data, Intelligent transportation systems, Soft computing techniques",

author = "Pedro Lopez-Garcia and Masegosa, \{Antonio D.\} and Enrique Onieva and Eneko Osaba",

note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 8th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2018 ; Conference date: 16-05-2018 Through 18-05-2018",

year = "2018",

doi = "10.1007/978-3-319-91641-5\_16",

language = "English",

isbn = "9783319916408",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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booktitle = "Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings",

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Lopez-Garcia, P, Masegosa, AD, Onieva, E & Osaba, E 2018, Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets: A comparative study. in N Melab, P Korosec & E-G Talbi (eds), Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10835 LNCS, Springer Verlag, pp. 185-196, 8th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2018, Paris, France, 16/05/18. https://doi.org/10.1007/978-3-319-91641-5_16

Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets: A comparative study. / Lopez-Garcia, Pedro; Masegosa, Antonio D.; Onieva, Enrique et al.
Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings. ed. / Nouredine Melab; Peter Korosec; El-Ghazali Talbi. Springer Verlag, 2018. p. 185-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10835 LNCS).

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

TY - GEN

T1 - Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets

T2 - 8th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2018

AU - Lopez-Garcia, Pedro

AU - Masegosa, Antonio D.

AU - Onieva, Enrique

AU - Osaba, Eneko

PY - 2018

Y1 - 2018

N2 - Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets.

AB - Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets.

KW - Classification

KW - Ensemble techniques

KW - Fuzzy techniques

KW - Imbalanced data

KW - Intelligent transportation systems

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DO - 10.1007/978-3-319-91641-5_16

M3 - Conference contribution

AN - SCOPUS:85047480495

SN - 9783319916408

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 185

EP - 196

BT - Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings

A2 - Melab, Nouredine

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PB - Springer Verlag

Y2 - 16 May 2018 through 18 May 2018

ER -

Lopez-Garcia P, Masegosa AD, Onieva E, Osaba E. Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets: A comparative study. In Melab N, Korosec P, Talbi EG, editors, Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Proceedings. Springer Verlag. 2018. p. 185-196. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-91641-5_16

Ensemble and fuzzy techniques applied to imbalanced traffic congestion datasets: A comparative study

Abstract

Publication series

Conference

UN SDGs

Keywords

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