Feature selection definition

1. What is meant by feature selection?
2. What is feature selection and filtering?
3. What is feature selection in CNN?
4. What is the aim of selecting features?

What is meant by feature selection?

Feature Selection is the method of reducing the input variable to your model by using only relevant data and getting rid of noise in data. It is the process of automatically choosing relevant features for your machine learning model based on the type of problem you are trying to solve.

What is feature selection and filtering?

In this method, features are filtered based on general characteristics (some metric such as correlation) of the dataset such correlation with the dependent variable. Filter method is performed without any predictive model. It is faster and usually the better approach when the number of features are huge.

What is feature selection in CNN?

Feature selection is an important technique to improve neural network performances due to the redundant attributes and the massive amount in original data sets. In this paper, a CNN with two convolutional layers followed by a dropout, then two fully connected layers, is equipped with a feature selection algorithm.

What is the aim of selecting features?

The objective of feature selection is to remove irrelevant and/or redundant features and retain only relevant features. Irrelevant features can be removed without affecting learning performance. Redundant features are a type of irrelevant features.