论文标题
一种新型的加权组合方法，用于使用模糊集的特征选择
A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets
论文作者
论文摘要
在本文中，我们提出了一种新型的加权组合特征选择方法，使用引导和模糊集。所提出的方法主要由三个过程组成，包括使用引导程序的模糊集生成，模糊集的加权组合以及基于Defuzzefience的功能排名。我们通过结合四种最先进的特征选择方法来实现提出的方法，并使用五倍的交叉验证根据三个公开可用的生物医学数据集进行了评估。基于特征选择结果，我们提出的方法与所有评估数据集的最佳单个特征选择方法产生了可比（如果不是更好的）分类精度。更重要的是，我们还应用了标准偏差和Pearson的相关性来衡量方法的稳定性。值得注意的是，当将变化和尺寸减小引入数据集时，我们的组合方法的稳定性明显高于四个单独的方法。
In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of fuzzy sets and feature ranking based on defuzzification. We implemented the proposed method by combining four state-of-the-art feature selection methods and evaluated the performance based on three publicly available biomedical datasets using five-fold cross validation. Based on the feature selection results, our proposed method produced comparable (if not better) classification accuracies to the best of the individual feature selection methods for all evaluated datasets. More importantly, we also applied standard deviation and Pearson's correlation to measure the stability of the methods. Remarkably, our combination method achieved significantly higher stability than the four individual methods when variations and size reductions were introduced to the datasets.
