An efficient fuzzy classifier with feature selection based on fuzzy entropy
By Hahn-Ming Lee, Chih-Ming Chen, Jyh-Ming Chen, Yuji Jou. IEEE Trans. Syst. Man Cybern. Part B, 2001
325 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/90460585b92e2af1a6b5ccc3cf91385a32c5a302
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
- Manuel Graña
- Michal Wozniak
- Qiang Shen
- Gamini Dissanayake
- Sarath Kodagoda
- Rami N. Khushaba
- Sara Lal
- Ren-hong Diao
In Computer Vision and Pattern Recognition
- Manuel Graña
- Michal Wozniak
- Qiang Shen
- Gamini Dissanayake
- Sarath Kodagoda
- Rami N. Khushaba
- Ren-hong Diao
- Emilio Corchado
Citations in the corpus
- Feature Selection With Harmony Search (2012, background): “Techniques such as text processing and classification [30] can benefit greatly from FS once the noisy, irrelevant, redundant, or misleading features”
- A survey of multiple classifier systems as hybrid systems (2014, methodology): “[103] used the fuzzy entropy measure to partition the feature space and select the relevant features with good separability for each of them.”
- Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2011, background): “F is an entropy measure if it satisf es the following Luca-Termini axioms [39], [43], [36]:”