Examples of 'feature selection' in a sentence
Meaning of "feature selection"
Feature selection refers to the process of identifying and selecting the most relevant or significant features or variables from a larger set of options. It is commonly used in fields such as data analysis, machine learning, and statistics. Feature selection helps to improve model performance, reduce complexity, and enhance interpretability
How to use "feature selection" in a sentence
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feature selection
A comparative study on feature selection in text categorization.
Feature selection also involved two steps.
Not able to achieve distributed feature selection.
It involves feature selection and feature extraction.
This can be seen as a form of automatic feature selection.
It consists of feature selection and feature extraction.
First is feature inference and automated feature selection.
Automatic feature selection for this task.
This process is called feature selection.
Automatic feature selection methods were used.
A simple bootstrapping technique was used for feature selection.
Feature selection was performed with an optimizing method.
Alternative methods based on feature selection have been proposed.
Feature selection techniques should be distinguished from feature extraction.
If you have automatic feature selection in your algorithm.
See also
Feature selection for classification of metal strip surface defects.
This is the assumption behind feature selection algorithms.
Feature selection is one of the important steps in a classification problem.
It then takes you to the feature selection screen.
Feature selection using associative memory paradigm and parallel computing.
Model and feature selection.
Determining a subset of the initial features is called feature selection.
It involves the aspects of feature selection and feature extraction.
The second part of the thesis focuses on feature selection.
Inherent in this feature selection process is the condensation of the data.
Overfitting pitfalls in feature selection.
Many feature selection methods are based on statistical analysis or neural network approaches.
Features and feature selection.
The second problem is a generalization of test cover for feature selection.
Different RBAs have been applied to feature selection in a variety of problem domains.
Feature selection is important to prevent unimportant attributes from using processor time.
These questions can often be formulated as feature selection problems.
This work investigates feature selection techniques to be applied to text classification task.
Dimension reduction can be divided into feature selection and extraction.
Feature selection for the purpose of segmentation of polysomnograms in subjects with developmental brain disorders.
This can be divided into feature selection and feature extraction.
Reducing dimensionality and identifying import features using advanced feature selection techniques.
Cluster acts directly on the feature selection step of the classification problem.
We investigate also in this part of the thesis a feature selection field.
Dimensionality reduction and feature selection can decrease variance by simplifying models.
The proposed method used random forest as classification algorithm and relieff for feature selection.
What deep learning does is it enables feature selection and model tuning.
This thesis develops feature selection procedures and gives some guarantees on their validity.
Internal copies of the data are used to perform feature selection and transformation.
Furthermore the feature selection results in a significant reduction of computation time and memory cost.
This dissertation presents a method for performing structural feature selection from process instances.
This thesis presents a novel feature selection technique that scales well to high dimensional feature spaces.
These methods differ in the way they consider label dependence within the feature selection process.
Some learning algorithms perform feature selection as part of their overall operation.
Filter feature selection is a specific case of a more general paradigm called Structure Learning.
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