Survey on Feature Selection
By Tarek Amr Abdallah, Beatriz de la Iglesia. 2015
255 citations recorded by the source corpus.
Feature selection plays an important role in the data mining process. It is needed to deal with the excessive number of features, which can become a computational burden on the learning algorithms. It is also necessary, even when computational resources are not scarce, since it improves the accuracy of the machine learning tasks, as we will see in the upcoming sections. In this review, we discuss the different feature selection approaches, and the relation between them and the various machine learning algorithms.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Wael AbdAlmageed
- Ayush Jaiswal
- Premkumar Natarajan
- Yue Wu
- Luisa Verdoliva
- Zhongyun Hua
- Xuefeng Hu
- Kiran Garimella
Citations in the corpus
- Unsupervised Adversarial Invariance (2018, methodology): “Methods for preventing supervised learning algorithms from learning false associations between target variables and nuisance factors have been studied from various perspectives including “feature selection” [16], “robustness through data augmentation” [10, 11] and “invariance induction” [3, 14, 19].”