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Feature Selection in Decision Systems: A Mean-Variance Approach

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  • Chengdong Yang
  • Wenyin Zhang
  • Jilin Zou
  • Shunbo Hu
  • Jianlong Qiu

Abstract

Uncertainty measure is an important implement for characterizing the degree of uncertainty. It has been extensively applied in pattern recognition and data clustering. Because of instability of traditional uncertainty measures, mean-variance measure (MVM) is utilized to perform feature selection, which could depress disturbances and noises effectively. Thereby, a novel evaluation function based on MVM is designed. The forward greedy search algorithm (FGSA) with the proposed evaluation function is exploited to perform feature selection. Experiment analysis shows the validity and effectiveness of MVM.

Suggested Citation

  • Chengdong Yang & Wenyin Zhang & Jilin Zou & Shunbo Hu & Jianlong Qiu, 2013. "Feature Selection in Decision Systems: A Mean-Variance Approach," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-8, May.
    • Handle: RePEc:hin:jnlmpe:268063
    DOI: 10.1155/2013/268063
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    Cited by:

    1. Khadija Attouri & Majdi Mansouri & Mansour Hajji & Abdelmalek Kouadri & Kais Bouzrara & Hazem Nounou, 2023. "Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

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