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- W4384695349 abstract "<p>Feature extraction plays an important role in accurate preprocessing and real-world applications. High-dimensional features in the data have a significant impact on the machine learning classification system. Relevant feature extraction is a fundamental step not only to reduce the dimensionality but also to improve the performance of the classifier. In this paper, the author proposes a hybrid dimensionality reduction technique using principal component analysis (PCA) and singular value decomposition (SVD) in a machine classification system with a support vector classifier (SVC). To evaluate the performance of PCSVD, the results are compared without using feature extraction techniques or with existing methods of independent component analysis (ICA), PCA, linear discriminant analysis (LDA), and SVD. In addition, the efficiency of the PCSVD method is measured on an increased scale of 1.54% accuracy, 2.70% sensitivity, 3.71% specificity, and 3.58% precision. In addition, reduce the 15% dimensionality and 40.60% RMSE, which are better than existing techniques found in the literature.</p>" @default.
- W4384695349 created "2023-07-20" @default.
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- W4384695349 date "2023-07-18" @default.
- W4384695349 modified "2023-09-26" @default.
- W4384695349 title "PCSVD: A hybrid feature extraction technique based on principal component analysis and singular value decomposition" @default.
- W4384695349 doi "https://doi.org/10.32629/jai.v6i2.586" @default.
- W4384695349 hasPublicationYear "2023" @default.
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