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
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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
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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 -
