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Feature Selection Using Zentropy-Based Uncertainty Measure
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AI
DOI：10.1109/TFUZZ.2023.3347757.png)
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摘要
En 中文
Feature selection and entropy theory are two efficacious data analysis tools for investigating uncertainty information processing in artificial intelligence. The fruitful marriage of the two has been an active research topic in knowledge discovery. Currently, most feature selection methods via entropy theory mainly focus on the information measures at a single granular level. However, it ignores the interaction between granular levels, which leads to the poor stability and accuracy of related methods. Hence, this article proposes a novel zentropy-based uncertainty measure to design a feature selection method by exploiting the granular level structure in knowledge space. Subsequently, by analyzing the granular level structure in decision data, the zentropy-based uncertainty measure and its properties are designed and analyzed to depict the uncertainty knowledge from whole and internal. Moreover, two importance measures are defined to evaluate features based on the designed uncertainty measure, and then a corresponding feature selection algorithm is developed. Finally, some experiments are carried out on public datasets to demonstrate that the proposed method can achieve state-of-the-art performance among methods, especially regarding stability and classification accuracy.
Keyword:
Uncertainty
Measurement uncertainty
Rough sets
Entropy
Information systems
Feature extraction
Current measurement
Decision information system
feature selection
granular computing
rough set (RS)
uncertainty measure
期刊
IF:
11.9
论文数:
5.0K
被引数:
2.9W
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引用论文
An Emerging Fuzzy Feature Selection Method Using Composite Entropy-Based Uncertainty Measure and Data Distribution一种新兴的基于复合熵的不确定性度量和数据分布的模糊特征选择方法
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