MIFS-ND: A mutual information-based feature selection method
By Nazrul Hoque, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita. Expert systems with applications, 2014
421 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/08428aed3842dbba5568e9e7a97074f691643a14
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
- Talari Ganesh
- Nenad D. Filipovic
- Mohamed F. Ghalwash
- Karla Brkic
- Hattan F. Abutarboush
- Ali Wagdy Mohamed
- Nikola Bogunovic
- Alan Jović
In Computer Vision and Pattern Recognition
- Karla Brkic
- Ali Wagdy Mohamed
- Nikola Bogunovic
- Alan Jović
- A. Taha
- Qing-Guo Wang
- Adeola Ogunleye
- Sharaf Jameel Malebary
Citations in the corpus
- Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “Generally, the techniques are classi-fied into three categories filter, wrapper and embedded meth-ods [12]–[15].”
- A review of feature selection methods with applications (2015, background): “Since exhaustive search for optimal feature subset is infeasible in most cases, many search strategies have been proposed in literature.”
- Minimum redundancy maximum relevance feature selection approach for temporal gene expression data (2017, methodology): “In [13] authors proposed the MIFS-ND algorithm, which selects features according to the minimal-redundancy-maximal-relevance criteria by using an optimization algorithm known as Non-dominated Sorting Genetic Algorithm-II [14].”