Filter Methods for Feature Selection - A Comparative Study
By Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, María Tombilla-Sanromán. Ideal, 2007
427 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems
View Paper: https://www.semanticscholar.org/paper/c168fe334a22b1d9f5d81ce6bdb78a0b9cda0ace
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Verónica Bolón-Canedo
- Amparo Alonso-Betanzos
- Noelia Sánchez-Maroño
- Anazida Binti Zainal
- Mohd Aizaini Maarof
- Aisha Abdallah
- Sonajharia Minz
- Vipin Kumar
In Computer Vision and Pattern Recognition
- Verónica Bolón-Canedo
- Amparo Alonso-Betanzos
- Noelia Sánchez-Maroño
- Anazida Binti Zainal
- Mohd Aizaini Maarof
- Aisha Abdallah
- Bilal Mirza
- Jie Wang
In Information Systems
- Anazida Binti Zainal
- Mohd Aizaini Maarof
- Aisha Abdallah
- Verónica Bolón-Canedo
- Amparo Alonso-Betanzos
- Noelia Sánchez-Maroño
Citations in the corpus
- A review of feature selection methods on synthetic data (2013, background): “[64] use a threshold defined by the largest gap between two consecutive ranked attributes, and other authors [51] just studied the whole ranking paying more attention to the first ranked features.”
- Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “In [73], various FS methods including CFS, IG, ReliefF, fast clustering-based feature selection algorithm (FAST) and support vector machine based on RFE (RFE-SVM) were employed to identify features with the highest classification accuracy, in the identification of breast cancer sub-types using prote”
- Fraud detection system: A survey (2016, methodology): “Embedded methods include variable selection as part of the training process without splitting the data into training and testing sets (Sánchez-Maroño et al., 2007).”