Authors :
Chava Mojesh Chowdary
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3fvTMZu
DOI :
https://doi.org/10.5281/zenodo.7278660
Abstract :
As a preprocessing step, dimensionality
reduction from high-dimensional data helps reduce
unnecessary data, enhance learning accuracy, and improve
result comprehensibility. However, the recent growth in
data dimensionality offers a serious challenge to the
efficiency and efficacy of many existing feature selection
and feature extraction approaches. Dimensionality
reduction is an essential topic in machine learning and
pattern recognition, and numerous algorithms have been
presented. In this research, certain commonly used feature
selection and feature extraction approaches are examined
to see how well they may be utilized to improve the
performance of learning algorithms and, as a result, the
predicted accuracy of clas N Ssifiers. A brief examination of
dimensionality reduction approaches is offered to
determine the strengths and limitations of various
commonly used dimensionality reduction methods
As a preprocessing step, dimensionality
reduction from high-dimensional data helps reduce
unnecessary data, enhance learning accuracy, and improve
result comprehensibility. However, the recent growth in
data dimensionality offers a serious challenge to the
efficiency and efficacy of many existing feature selection
and feature extraction approaches. Dimensionality
reduction is an essential topic in machine learning and
pattern recognition, and numerous algorithms have been
presented. In this research, certain commonly used feature
selection and feature extraction approaches are examined
to see how well they may be utilized to improve the
performance of learning algorithms and, as a result, the
predicted accuracy of clas N Ssifiers. A brief examination of
dimensionality reduction approaches is offered to
determine the strengths and limitations of various
commonly used dimensionality reduction methods
