On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets

Salah Eddine Henouda; Fatima Zohra Laallam; Okba Kazar; Saad Harous; Djihane Houfani

doi:10.1007/978-3-031-25344-7_15

On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets

Salah Eddine Henouda
, Fatima Zohra Laallam
, Okba Kazar
, Saad Harous
, Djihane Houfani

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

2 Citations (Scopus)

Abstract

The present work strives to investigate the effect of using dimensionality reduction techniques (DRTs) on breast cancer (BC) classification problem. Primarily, we focused on the following five (DRTs): Auto-Encoders (AE), T-Distributed Stochastic Neighbor Embedding (T- SNE), Recursive Feature Elimination (RFE), Isometric Feature Mapping (Isomap), and Principle Component analysis (PCA). These methods are combined with two famous classifiers that are Support Vector Machine (SVM) and Multilayer perceptron (MLP). They are used for BC classification. Breast Cancer Wisconsin Diagnostic (WDBC) data set was used to validate the experiments of this work. The former was provided by the University of California, Irvine (UCI) machine learning repository. The results demonstrated that combining MLP with the chosen (DRTs) methods increased the classification accuracy for almost all built models by at least 0.7%. In addition, they revealed a decrease in the classification accuracy using SVM as a classifier for almost all built models.

Original language	English
Title of host publication	12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System
Editors	Mohamed Ridda Laouar, Valentina Emilia Balas, Brahim Lejdel, Sean Eom, Mohamed Amine Boudia
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	156-166
Number of pages	11
ISBN (Print)	9783031253430
DOIs	https://doi.org/10.1007/978-3-031-25344-7_15
Publication status	Published - 2023
Externally published	Yes
Event	12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022 - Virtual, Online Duration: Aug 26 2022 → Aug 27 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	624 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022
City	Virtual, Online
Period	8/26/22 → 8/27/22

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Breast cancer prediction
Deep learning
Dimensionality reduction
Machine learning
Medical dataset
Neural networks

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-25344-7_15

Cite this

Henouda, S. E., Laallam, F. Z., Kazar, O., Harous, S., & Houfani, D. (2023). On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets. In M. R. Laouar, V. E. Balas, B. Lejdel, S. Eom, & M. A. Boudia (Eds.), 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System (pp. 156-166). (Lecture Notes in Networks and Systems; Vol. 624 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-25344-7_15

On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets. / Henouda, Salah Eddine; Laallam, Fatima Zohra; Kazar, Okba et al.
12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System. ed. / Mohamed Ridda Laouar; Valentina Emilia Balas; Brahim Lejdel; Sean Eom; Mohamed Amine Boudia. Springer Science and Business Media Deutschland GmbH, 2023. p. 156-166 (Lecture Notes in Networks and Systems; Vol. 624 LNNS).

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

Henouda, SE, Laallam, FZ, Kazar, O, Harous, S & Houfani, D 2023, On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets. in MR Laouar, VE Balas, B Lejdel, S Eom & MA Boudia (eds), 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System. Lecture Notes in Networks and Systems, vol. 624 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 156-166, 12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022, Virtual, Online, 8/26/22. https://doi.org/10.1007/978-3-031-25344-7_15

Henouda SE, Laallam FZ, Kazar O, Harous S, Houfani D. On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets. In Laouar MR, Balas VE, Lejdel B, Eom S, Boudia MA, editors, 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System. Springer Science and Business Media Deutschland GmbH. 2023. p. 156-166. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-25344-7_15

Henouda, Salah Eddine ; Laallam, Fatima Zohra ; Kazar, Okba et al. / On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets. 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System. editor / Mohamed Ridda Laouar ; Valentina Emilia Balas ; Brahim Lejdel ; Sean Eom ; Mohamed Amine Boudia. Springer Science and Business Media Deutschland GmbH, 2023. pp. 156-166 (Lecture Notes in Networks and Systems).

@inproceedings{bfb8d4c02c2d4ad894b0d3c66b18219b,

title = "On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets",

abstract = "The present work strives to investigate the effect of using dimensionality reduction techniques (DRTs) on breast cancer (BC) classification problem. Primarily, we focused on the following five (DRTs): Auto-Encoders (AE), T-Distributed Stochastic Neighbor Embedding (T- SNE), Recursive Feature Elimination (RFE), Isometric Feature Mapping (Isomap), and Principle Component analysis (PCA). These methods are combined with two famous classifiers that are Support Vector Machine (SVM) and Multilayer perceptron (MLP). They are used for BC classification. Breast Cancer Wisconsin Diagnostic (WDBC) data set was used to validate the experiments of this work. The former was provided by the University of California, Irvine (UCI) machine learning repository. The results demonstrated that combining MLP with the chosen (DRTs) methods increased the classification accuracy for almost all built models by at least 0.7\%. In addition, they revealed a decrease in the classification accuracy using SVM as a classifier for almost all built models.",

keywords = "Breast cancer prediction, Deep learning, Dimensionality reduction, Machine learning, Medical dataset, Neural networks",

author = "Henouda, \{Salah Eddine\} and Laallam, \{Fatima Zohra\} and Okba Kazar and Saad Harous and Djihane Houfani",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022 ; Conference date: 26-08-2022 Through 27-08-2022",

year = "2023",

doi = "10.1007/978-3-031-25344-7\_15",

language = "English",

isbn = "9783031253430",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "156--166",

editor = "Laouar, \{Mohamed Ridda\} and Balas, \{Valentina Emilia\} and Brahim Lejdel and Sean Eom and Boudia, \{Mohamed Amine\}",

booktitle = "12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System",

}

TY - GEN

T1 - On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets

AU - Henouda, Salah Eddine

AU - Laallam, Fatima Zohra

AU - Kazar, Okba

AU - Harous, Saad

AU - Houfani, Djihane

PY - 2023

Y1 - 2023

N2 - The present work strives to investigate the effect of using dimensionality reduction techniques (DRTs) on breast cancer (BC) classification problem. Primarily, we focused on the following five (DRTs): Auto-Encoders (AE), T-Distributed Stochastic Neighbor Embedding (T- SNE), Recursive Feature Elimination (RFE), Isometric Feature Mapping (Isomap), and Principle Component analysis (PCA). These methods are combined with two famous classifiers that are Support Vector Machine (SVM) and Multilayer perceptron (MLP). They are used for BC classification. Breast Cancer Wisconsin Diagnostic (WDBC) data set was used to validate the experiments of this work. The former was provided by the University of California, Irvine (UCI) machine learning repository. The results demonstrated that combining MLP with the chosen (DRTs) methods increased the classification accuracy for almost all built models by at least 0.7%. In addition, they revealed a decrease in the classification accuracy using SVM as a classifier for almost all built models.

AB - The present work strives to investigate the effect of using dimensionality reduction techniques (DRTs) on breast cancer (BC) classification problem. Primarily, we focused on the following five (DRTs): Auto-Encoders (AE), T-Distributed Stochastic Neighbor Embedding (T- SNE), Recursive Feature Elimination (RFE), Isometric Feature Mapping (Isomap), and Principle Component analysis (PCA). These methods are combined with two famous classifiers that are Support Vector Machine (SVM) and Multilayer perceptron (MLP). They are used for BC classification. Breast Cancer Wisconsin Diagnostic (WDBC) data set was used to validate the experiments of this work. The former was provided by the University of California, Irvine (UCI) machine learning repository. The results demonstrated that combining MLP with the chosen (DRTs) methods increased the classification accuracy for almost all built models by at least 0.7%. In addition, they revealed a decrease in the classification accuracy using SVM as a classifier for almost all built models.

KW - Breast cancer prediction

KW - Deep learning

KW - Dimensionality reduction

KW - Machine learning

KW - Medical dataset

KW - Neural networks

UR - https://www.scopus.com/pages/publications/85148035115

UR - https://www.scopus.com/pages/publications/85148035115#tab=citedBy

U2 - 10.1007/978-3-031-25344-7_15

DO - 10.1007/978-3-031-25344-7_15

M3 - Conference contribution

AN - SCOPUS:85148035115

SN - 9783031253430

T3 - Lecture Notes in Networks and Systems

SP - 156

EP - 166

BT - 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System

A2 - Laouar, Mohamed Ridda

A2 - Balas, Valentina Emilia

A2 - Lejdel, Brahim

A2 - Eom, Sean

A2 - Boudia, Mohamed Amine

PB - Springer Science and Business Media Deutschland GmbH

T2 - 12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022

Y2 - 26 August 2022 through 27 August 2022

ER -

On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this