INFRARED, Volume. 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
Author Affiliations
  • [in Chinese]
  • show less
    References(6)

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

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

    [5] [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] [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] [8] Li J. Divergence measures based on the Shannon entropy[J]. IEEE Transactions on Information Theory, 1991, 37(1): 145-151.

    [9] [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.

    Tools

    Get Citation

    Copy Citation Text

    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

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Nov. 9, 2020

    Accepted: --

    Published Online: Aug. 16, 2021

    The Author Email: Zi-jing LV (570824026@qq.com)

    DOI:10.3969/j.issn.1672-8785.2021.05.006

    Topics