A Novel Information Theoretic Cost Measure for Filtering Based Feature Selection from Hyperspectral Images
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Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-15-4015-8_10
Reference16 articles.
1. Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Networks 5(4), 537–550 (1994). https://doi.org/10.1109/72.298224
2. Baumgardner, M.F., Biehl, L.L., Landgrebe, D.A.: 220 band aviris hyperspectral image data set: June 12, 1992 Indian pine test site 3, September 2015. https://doi.org/10.4231/R7RX991C
3. Cheng-San, Y., Chuang, L.Y., Ke, C.H., Yang, C.H.: A hybrid feature selection method for microarray classification. IAENG Int. J. Comput. Sci. 35 (2008)
4. Chicharro, D., Panzeri, S.: Synergy and redundancy in dual decompositions of mutual information gain and information loss. Entropy 19(2), 71 (2017). https://doi.org/10.3390/e19020071
5. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. In: Computational Systems Bioinformatics. Proceedings of the 2003 IEEE Bioinformatics Conference, CSB2003, August 2003, pp. 523–528 (2003). https://doi.org/10.1109/CSB.2003.1227396
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