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 proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publication12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” - Intelligent Information, Data Science and Decision Support System
EditorsMohamed Ridda Laouar, Valentina Emilia Balas, Brahim Lejdel, Sean Eom, Mohamed Amine Boudia
PublisherSpringer Science and Business Media Deutschland GmbH
Pages156-166
Number of pages11
ISBN (Print)9783031253430
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022 - Virtual, Online
Duration: Aug 26 2022Aug 27 2022

Publication series

NameLecture Notes in Networks and Systems
Volume624 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022
CityVirtual, Online
Period8/26/228/27/22

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

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