mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification
By Alper Ünler, Alper Ekrem Murat, Ratna Babu Chinnam. Information Sciences, 2011
298 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/e2201e38918c6b736ad533baca29cdced2ff7cc0
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
- Ruinan Sun
- Sina Tabakhi
- Muhammad Aliyu Sulaiman
- Jianping Li
- Nazrul Hoque
- Amin Ul Haq
- Muhammad Hammad Memon
- Parham Moradi
In Computer Vision and Pattern Recognition
- Ruinan Sun
- Sina Tabakhi
- Muhammad Aliyu Sulaiman
- Jianping Li
- Nazrul Hoque
- Amin Ul Haq
- Muhammad Hammad Memon
- Parham Moradi
Citations in the corpus
- An unsupervised feature selection algorithm based on ant colony optimization (2014, methodology): “Wisconsin Diagnostic Breast Cancer (WDBC), Ionosphere, Dermatology, SpamBase and Arrhythmia from UCI repository and Madelon and Arcene from NIPS2003 feature selection challenge, which have been extensively used in the literature (Ferreira and Figueiredo, 2012; Gheyas and Smith, 2010; Martínez Sotoca”
- Feature selection based on mutual information (2015, methodology): “Unler et al in [4] introduces hybrid filter-wrapper feature subset selection algorithm based on PSO with SVM.”
- MIFS-ND: A mutual information-based feature selection method (2014, background): “Two important aspects of feature selection are: (i) minimum redundancy and (ii) maximum relevance [6].”
- A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms (2018, methodology): “0e pseudocode mRMR algorithm is described in [25].”