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- W4319065719 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." @default.
- W4319065719 created "2023-02-04" @default.
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- W4319065719 date "2023-01-01" @default.
- W4319065719 modified "2023-09-25" @default.
- W4319065719 title "On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets" @default.
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- W4319065719 doi "https://doi.org/10.1007/978-3-031-25344-7_15" @default.
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