A review of feature selection methods with applications

By Alan Jović, Karla Brkic, Nikola Bogunovic. International Convention on Information and Communication Technology, Electronics and Microelectronics, 2015

1,007 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/6dc7cc7ec2c016ddcf341f868bafe459ca156ab9

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

1. A. Taha
2. Talari Ganesh
3. Hattan F. Abutarboush
4. Qing-Guo Wang
5. Ali Wagdy Mohamed
6. Prachi Agrawal
7. Adeola Ogunleye
8. Sharaf Jameel Malebary

In Computer Vision and Pattern Recognition

1. A. Taha
2. Qing-Guo Wang
3. Ali Wagdy Mohamed
4. Adeola Ogunleye
5. Sharaf Jameel Malebary

In Information Systems

1. A. Taha
2. Talari Ganesh
3. Hattan F. Abutarboush
4. Qing-Guo Wang
5. Ali Wagdy Mohamed
6. Prachi Agrawal
7. Adeola Ogunleye
8. Sharaf Jameel Malebary

Citations in the corpus

• Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, background): “Jovic et al. [18] differentiates search techniques into three categories; exponential, sequential and randomized selection strategy.”
• XGBoost Model for Chronic Kidney Disease Diagnosis (2020, methodology): “Univariate Selection works by selecting features that have performed well in accordance to univariate statistical tests such as Pearsons Correlation, LDA, ANOVA and Chi-Square [35].”
• An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine (2020, methodology): “Because of its computational efficiency and leading performance in terms of precision [44], information gain is employed as a feature selection method in the proposed approach.”