Salem Omar A M, Liu Feng, Chen Yi-Ping Phoebe, Chen Xi
School of Computer Science, Wuhan University, Wuhan 430072, China.
Department of Information System, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt.
Entropy (Basel). 2020 Jul 9;22(7):757. doi: 10.3390/e22070757.
The main challenge of classification systems is the processing of undesirable data. Filter-based feature selection is an effective solution to improve the performance of classification systems by selecting the significant features and discarding the undesirable ones. The success of this solution depends on the extracted information from data characteristics. For this reason, many research theories have been introduced to extract different feature relations. Unfortunately, traditional feature selection methods estimate the feature significance based on either individually or dependency discriminative ability. This paper introduces a new ensemble feature selection, called fuzzy feature selection based on relevancy, redundancy, and dependency (FFS-RRD). The proposed method considers both individually and dependency discriminative ability to extract all possible feature relations. To evaluate the proposed method, experimental comparisons are conducted with eight state-of-the-art and conventional feature selection methods. Based on 13 benchmark datasets, the experimental results over four well-known classifiers show the outperformance of our proposed method in terms of classification performance and stability.
分类系统的主要挑战在于对不良数据的处理。基于过滤器的特征选择是一种有效的解决方案，通过选择重要特征并丢弃不良特征来提高分类系统的性能。该解决方案的成功取决于从数据特征中提取的信息。因此，人们引入了许多研究理论来提取不同的特征关系。不幸的是，传统的特征选择方法要么基于个体判别能力，要么基于依赖判别能力来估计特征重要性。本文介绍了一种新的集成特征选择方法，称为基于相关性、冗余性和依赖性的模糊特征选择（FFS-RRD）。该方法同时考虑个体判别能力和依赖判别能力，以提取所有可能的特征关系。为了评估该方法，我们与八种先进的传统特征选择方法进行了实验比较。基于13个基准数据集，在四个著名分类器上的实验结果表明，我们提出的方法在分类性能和稳定性方面表现出色。
