JCIF Feature Selection Method

Versione 1.0.0 (6,01 KB) da U A Md Ehsan

This function implements the JCIF feature selection method

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Aggiornato 24 dic 2025

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This repository provides a MATLAB implementation of the Joint Conditional Mutual Information for Selecting Informative Feature (JCIF) selection criterion for supervised feature selection.

JCIF is an information-theoretic method that incrementally selects features by jointly considering:

Redundency handled using conditional mutual information, and Relevence via joint mutual information.

The method is designed for discrete-valued data and is particularly suitable for high-dimensional feature spaces.

Cita come

Ali, U. A. Md Ehsan, and Keisuke Kameyama. “Informative Band Subset Selection for Hyperspectral Image Classification Using Joint and Conditional Mutual Information.” 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2022, pp. 573–80, https://doi.org/10.1109/ssci51031.2022.10022154.

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Versione	Pubblicato	Note della release
1.0.0	24 dic 2025		Scarica

MLA	Ali, U. A. Md Ehsan, and Keisuke Kameyama. “Informative Band Subset Selection for Hyperspectral Image Classification Using Joint and Conditional Mutual Information.” 2022 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2022, pp. 573–80, https://doi.org/10.1109/ssci51031.2022.10022154.
APA	Ali, U. A. M. E., & Kameyama, K. (2022). Informative Band Subset Selection for Hyperspectral Image Classification using Joint and Conditional Mutual Information. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 573–580). IEEE. Retrieved from http://dx.doi.org/10.1109/SSCI51031.2022.10022154
BibTeX	@inproceedings{Ali_2022, title={Informative Band Subset Selection for Hyperspectral Image Classification using Joint and Conditional Mutual Information}, url={http://dx.doi.org/10.1109/SSCI51031.2022.10022154}, DOI={10.1109/ssci51031.2022.10022154}, booktitle={2022 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Ali, U A Md Ehsan and Kameyama, Keisuke}, year={2022}, month=dec, pages={573–580} }