A SURVEY ON FEATURE SELECTION TECHNIQUES
Keywords:
Text mining, Text classification, Filter,, WrapperAbstract
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysis and processing which also focuses on identifying irrelevant information without affecting the accuracy of the classifier.FS selects a subset of relevantfeatures andremoves irrelevant and redundant features from the raw data to builda robust learning model. FS is very important, not only because of the curse of dimensionality, but also because ofdata complexities andthequantities of the data faced by multiple disciplines, such as machine learning, data mining,statistics,pattern recognition andbioinformatics. In recent years, we have seen extensiveresearch infeature selectionwhichhas been expandingin depth and in breadthfrom simple to more advanced techniques, from supervised to unsupervised and semi-supervised feature selection.This paper presentsa state-of-art survey of feature selection techniques.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Under the Creative Commons Attribution- 4.0 International License (CC BY-4.0 DEED).
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
