Hybrid feature selection by combining filters and wrappers

By Hui-Huang Hsu, Cheng-Wei Hsieh, Ming-Da Lu. Expert systems with applications, 2011

356 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5b8eb510fb15f3c948917c8f791a090f3263bb8e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Christine Dewi
2. Rung Ching Chen
3. Nazrul Hoque
4. Jugal Kumar Kalita
5. Rezzy Eko Caraka
6. Dhruba Kumar Bhattacharyya
7. Su-Wen Huang

Citations in the corpus

• MIFS-ND: A mutual information-based feature selection method (2014, methodology): “Feature selection, also known as variable, attribute, or variable subset selection is used in machine learning or statistics for selection of a subset of features to construct models for describing data (Arauzo-Azofra, Aznarte, & Benítez, 2011; Cadenas, Garrido, & MartíNez, 2013; Dash & Liu, 1997;…”
• Selecting critical features for data classification based on machine learning methods (2020, background): “It is beneficial in improving computational speed and prediction accuracy [20].”