Feature Space Reduction, using PCA in the Algorithm for Epilepsy Detection, using an Adaptive Neuro-Fuzzy Inference System and Comparative Analysis

Stoimchev, Marjan; Latkoska, Vesna Ojleska

doi:10.12700/aph.17.10.2020.10.6

Cited by 4 publications

(1 citation statement)

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“…When selecting the optimal features of the data, the PCA algorithm is used to calculate the relevant employment data in order to reduce the data dimensionality and thus select the best features [17][18][19]. In stage 1 of the process, the PCA algorithm is mainly used to calculate and analyze the covariance of the employment data features.…”

Section: Dimensionality Reduction Operation Methods For Feature Selec...mentioning

confidence: 99%

Research on Expanding Grassroots Employment Space for College Students in the Context of Rural Revitalization + Data Platform

Wei

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, in the context of rural revitalization + data platform, a grassroots employment platform for college students is designed according to the data architecture, and the data is processed hierarchically to achieve the organization and knowledge accumulation of cross-domain employment data, which makes multiple employment data standardized. Using data analysis parallelization algorithm and dimensionality reduction operation method, the training data are read and loaded into the distributed memory of the employment data platform to select the best employment features. According to the design platform of this paper, the employment rate for those employed in the eastern region and 80.16% in the central region has reached 91.15%. This paper's design model can improve employee efficiency and expand the grassroots employment space of college students, as indicated.

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Section: Dimensionality Reduction Operation Methods For Feature Selec...mentioning

confidence: 99%