Published in Engineering Applications of Artificial Intelligence, 2022
A new hybrid feature selection framework named the HyCluster is proposed.
Recommended citation: M.A.Ganjei, R.Boostani, (2022). "A Hybrid Feature Selection Scheme for High-Dimensional Data." Engineering Applications of Artificial Intelligence. Volume 113,2022, 104894, https://doi.org/10.1016/j.engappai.2022.104894 https://doi.org/10.1016/j.engappai.2022.104894
Published in IEEE 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2020
In this paper, we proposed a new hybrid feature selection method
Recommended citation: M. A. Ganjei and R. Boostani, "A Fast Hybrid Feature Selection Method," 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019, pp. 6-11, doi: 10.1109/ICCKE48569.2019.8964884. https://ieeexplore.ieee.org/document/8964884
Published in Akhtar Publication, 2020
This book focuses more on feature selection in supervised learning systems and the final chapters on feature extraction and unsupervised learning.
Recommended citation: M.A.Ganjei, K.Shirini, (2020). "Feature Selection and Dimension Reduction in Machine Learning." Akhtar Publication. https://mohammadahmadig.github.io/publication/2020-09-02-book-1
Published in 3rd national conference on Computer, Information Technology and Artificial Intelligence, 2020
In this paper, fingerprint gender recognition using a combination of three feature vectors was used to extract features to classify the gender of persons.
Recommended citation: K.Shirini, N.R.Zamir M.A.Ganjei, MR.Feizi-Derakhshi(2020). "Improving Gender Recognition Using Fingerprint with SVM, KNN, and Decision Tree." 3rd national conference on Computer, Information Technology and Artificial Intelligence. https://en.civilica.com/doc/1015568/