Editing Feature selection (section)

====Filter method====
[[File:Filter Methode.png|thumb|300px|Filter Method for feature selection]]
Filter type methods select variables regardless of the model. They are based only on general features like the correlation with the variable to predict. Filter methods suppress the least interesting variables. The other variables will be part of a classification or a regression model used to classify or to predict data. These methods are particularly effective in computation time and robust to overfitting.<ref name="ReferenceA">{{cite thesis |first=Julie |last=Hamon |title=Optimisation combinatoire pour la sélection de variables en régression en grande dimension: Application en génétique animale |url=https://tel.archives-ouvertes.fr/tel-00920205 |date=November 2013 |publisher=[[Lille University of Science and Technology]] |language=fr }}</ref>

Filter methods tend to select redundant variables when they do not consider the relationships between variables. However, more elaborate features try to minimize this problem by removing variables highly correlated to each other, such as the Fast Correlation Based Filter (FCBF) algorithm.<ref>{{Cite journal |first1=Lei |last1=Yu |first2=Huan |last2=Liu |title=Feature selection for high-dimensional data: a fast correlation-based filter solution |journal=ICML'03: Proceedings of the Twentieth International Conference on International Conference on Machine Learning |date=August 2003 |pages=856–863 |url=https://www.aaai.org/Papers/ICML/2003/ICML03-111.pdf }}</ref>