Lakhasly

.The method's goal is to ensure optimal class separability by transforming characteristics from a high-dimensional space into a lower-dimensional space inwhichtheproportionofbetween-classvariation to within class variance is maximized [31,32].Improved Linear Discriminant Analysis Linear discriminant analysis is a method for simplifying classification problems in supervised machine learning.3.2.

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3.2. Improved Linear Discriminant Analysis
Linear discriminant analysis is a method for simplifying classification problems in
supervised machine learning. Using it requires distinguishing between two or more
categories; thus, it is employed for modeling set differences. It is a tool for bringing a feature
from one dimension down to a lower-dimensional space. The method’s goal is to ensure
optimal class separability by transforming characteristics from a high-dimensional space
into a lower-dimensional space inwhichtheproportionofbetween-classvariation to within
class variance is maximized [31,32]. Because of its widespread use in the preprocessing
of machine-learning classification applications and its ability to transform features into
a lower dimensional space by minimizing the ratio of the between-class variance to the
within-class variance,