The main objectives of data analysis is choose/select useful/optimal features of concerns for object recognition, detection or classification. The recognition of object or phenomena may be analyzed via various models as well as many data smoothing/cleaning/dimension-reduction/linearization etc., type data pre-processing techniques for optimal results of more productivity/yield/optimization/shortest-way-of reaching-to-the-destinations via minimum inputs. In data analysis we select the features of concerns for optimization, whilst we reject/reduce the features(the domain variables = dimensions) for enhancing computational acceleration or saving time. Dimension reduction is a kind of trimming as we do in many other areas of practicability for high visualization with more productivity.

feature selection we select the optimum bands for our application

feature reduction is a dimension reduction as we do with hyperspectral data to reduce the hundred of bands

Feature Selection vs Dimensionality Reduction

Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension.

Dear Mahit,

Some advantages of features selection include:

1) facilitates the model interpretability. Generally, a model with few variables may be easy to be explained.

2) Helps in removing highly correlated variables (problem of collinearity)

Dear Mahit Kumar Paul

This paper can answer your question

Article Wind Speed Ensemble Forecasting Based on Deep Learning Using...

Feature Selection versus Feature Reduction

-Dimensionality (Feature ) reduction:

Dimensionality reduction is a method of converting the high dimensional

variables into lower-dimensional variables.

-Examples of Dimension reduction techniques are

1) PCA (Principal Component Analysis )

2) LDA (Linear Discriminant Analysis)

-Feature selection - is a process that selects and best features without modifying them at all.

For more details please go through the following links

Lecture-42: Dimensionality Reduction: Principal Component Analysis

https://www.youtube.com/watch?v=c8MyQVfXW6s

Lecture-44: Feature Selection In Machine Learning

https://www.youtube.com/watch?v=9QzD-c-8wak&t=1072s

Lecture-47: Linear Discriminant Analysis for Feature Reduction

https://www.youtube.com/watch?v=rX9J5qti3aU&t=1041s

The following article includes a part of the answer.

Article B-MFO: A Binary Moth-Flame Optimization for Feature Selectio...

In the case of feature selection, you have to select the features that the result depends on it and then neglect unwanted features for gaining accurate results, fast processing and etc.

on the other hand, feature reduction is a dimension reduction.

Mahit Kumar Paul Feature selection is utmost important for achieving a targeted objectives/goals while the features reduction means to drop the un necessary features for make best and efficient evaluation of achieved objectives.