What is it about?
Meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods. To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality. We contributed to meeting the challenges of big data visualization using the embedded method based “Select from model (SFM)” method by using “Random forest Importance algorithm (RFI)” and comparing it with the filter method by using “Select percentile (SP)” method based chi square “Chi2” tool for selecting the most important features, which are then fed into a classification process using the logistic regression (LR) algorithm and the k-nearest neighbor (KNN) algorithm. Thus, the classification accuracy (AC) performance of LR is also compared to the KNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied.
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Why is it important?
Because it faces the challenges of visualizing big data in a modern way
Perspectives
The author and I were very happy writing this article because we are happy to solve the big problem of big data visualization to help the world better understand big data. This distinguished scientific research is due to the use of a new method to meet the challenges of visualizing big data in a distinctive and rare way I hope everyone benefits from this article
Luay Thamer
Read the Original
This page is a summary of: Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges, Computers Materials & Continua, January 2023, Tsinghua University Press,
DOI: 10.32604/cmc.2023.032287.
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