论文标题
基于模糊熵的特征选择和分类框架的性能优化
Performance Optimization of a Fuzzy Entropy based Feature Selection and Classification Framework
论文作者
论文摘要
在本文中，基于模糊的熵特征选择框架，已经实现了不同的方法并进行了比较以改善框架的关键组成部分。这些方法包括三个理想矢量计算，三个最大相似性分类器和三个模糊熵函数的组合。还比较了基于模糊熵值的不同特征去除顺序。对三个公开可用的生物医学数据集进行了评估。从实验中，我们得出了理想矢量，相似性分类器和模糊熵函数的优化组合，用于特征选择。还将优化的框架与其他六个经典的基于过滤器的特征选择方法进行了比较。所提出的方法与相关性和relieff方法一起被评为最佳性能者之一。更重要的是，当功能逐渐删除时，所提出的方法在所有三个数据集中都达到了最稳定的性能。这表明比其他比较方法更好的功能排名性能。
In this paper, based on a fuzzy entropy feature selection framework, different methods have been implemented and compared to improve the key components of the framework. Those methods include the combinations of three ideal vector calculations, three maximal similarity classifiers and three fuzzy entropy functions. Different feature removal orders based on the fuzzy entropy values were also compared. The proposed method was evaluated on three publicly available biomedical datasets. From the experiments, we concluded the optimized combination of the ideal vector, similarity classifier and fuzzy entropy function for feature selection. The optimized framework was also compared with other six classical filter-based feature selection methods. The proposed method was ranked as one of the top performers together with the Correlation and ReliefF methods. More importantly, the proposed method achieved the most stable performance for all three datasets when the features being gradually removed. This indicates a better feature ranking performance than the other compared methods.
