An Optimal Spectral Feature Selection Algorithm Based on Zero Loss Redundancy Reduction Strategy

Zi-jing LV; Peng ZHANG; Zhi-ming LIU; Zhi-hui ZHANG; Qiang Han

doi:10.3969/j.issn.1672-8785.2021.05.006

Journals >INFRARED >Volume 42 >Issue 5 >Page 33 > Article

INFRARED
Vol. 42, Issue 5, 33 (2021)

An Optimal Spectral Feature Selection Algorithm Based on Zero Loss Redundancy Reduction Strategy

Zi-jing LV^*, Peng ZHANG, Zhi-ming LIU, Zhi-hui ZHANG, and Qiang Han

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[in Chinese]

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DOI: 10.3969/j.issn.1672-8785.2021.05.006 Cite this Article

LV Zi-jing, ZHANG Peng, LIU Zhi-ming, ZHANG Zhi-hui, Han Qiang. An Optimal Spectral Feature Selection Algorithm Based on Zero Loss Redundancy Reduction Strategy[J]. INFRARED, 2021, 42(5): 33 Copy Citation Text

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References

[1] Quinlan J R. Induction of Decision Trees[J]. Machine Learning, 1986, 4(2): 81-106.

[3] Miyahara K, Pazzani M J. Collaborative filtering with the simple Bayesian classifier[C]. Heidelberg： Pacific Rim International Conference on Artificial Intelligence, 2000.

[5] Hall M A. Correlation-based feature selection for discrete and numeric class machine learning[C]. Los Altos: 7th International Conference on Machine Learning, 2000.

[6] Ding C, Peng H. Minimum Redundancy Feature Selection from Microarray Gene Expression Data[C]. San Francisco: IEEE Computer Society Conference on Bioinformatics, 2003.

[8] Li J. Divergence measures based on the Shannon entropy[J]. IEEE Transactions on Information Theory, 1991, 37(1): 145-151.

[9] Yu L, Liu H. Efficient feature selection via analysis of relevance and redundancy[J]. Journal of Machine Learning Research, 2004, 5(12): 1205-1224.

LV Zi-jing, ZHANG Peng, LIU Zhi-ming, ZHANG Zhi-hui, Han Qiang. An Optimal Spectral Feature Selection Algorithm Based on Zero Loss Redundancy Reduction Strategy[J]. INFRARED, 2021, 42(5): 33

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