An Extensive Simulation Study for Evaluation of Penalized Variable Selection Methods in Logistic Regression Model with High Dimensional Data

Author:

Sancar NuriyeORCID,Bacar AyadORCID

Publisher

Springer Nature Switzerland

Reference19 articles.

1. Gnana, D.A.A., Balamurugan, S.A.A., Leavline, E.J.: Literature review on feature selection methods for high-dimensional data. Int. J. Comput. Appl. 136, 9–17 (2016)

2. Silaich, S., Gupta, S.: Feature selection in high dimensional data: a review. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds.) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol. 608. Springer, Singapore (2023)

3. Algamal, Z.Y., Lee, M.H.: A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification. Adv. Data Anal. Classif. 13, 753–771 (2019)

4. Biswas, S., Bordoloi, M., Purkayastha, B.: Review on feature selection and classification using neuro-fuzzy approaches. Int. J. Appl. Evol. Comput. 7, 28–44 (2016)

5. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

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