School of Computer Science, Wuhan University, Wuhan 430072, China.
Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt.
Math Biosci Eng. 2020 Nov 30;18(1):305-327. doi: 10.3934/mbe.2021016.
Nowadays, real-world applications handle a huge amount of data, especially with high-dimension features space. These datasets are a significant challenge for classification systems. Unfortunately, most of the features present are irrelevant or redundant, thus making these systems inefficient and inaccurate. For this reason, many feature selection (FS) methods based on information theory have been introduced to improve the classification performance. However, the current methods have some limitations such as dealing with continuous features, estimating the redundancy relations, and considering the outer-class information. To overcome these limitations, this paper presents a new FS method, called Fuzzy Joint Mutual Information Maximization (FJMIM). The effectiveness of our proposed method is verified by conducting an experimental comparison with nine of conventional and state-of-the-art feature selection methods. Based on 13 benchmark datasets, experimental results confirm that our proposed method leads to promising improvement in classification performance and feature selection stability.
如今，现实世界中的应用处理着大量的数据，尤其是具有高维特征空间的数据。这些数据集对分类系统来说是一个重大的挑战。不幸的是，大多数出现的特征都是不相关或冗余的，因此使得这些系统效率低下且不准确。出于这个原因，已经引入了许多基于信息论的特征选择 (FS) 方法来提高分类性能。然而，当前的方法存在一些局限性，例如处理连续特征、估计冗余关系以及考虑外部类信息。为了克服这些局限性，本文提出了一种新的 FS 方法，称为模糊联合互信息最大化 (FJMIM)。通过与九种传统和最先进的特征选择方法进行实验比较，验证了我们提出的方法的有效性。基于 13 个基准数据集，实验结果证实，我们提出的方法在分类性能和特征选择稳定性方面都有显著的提高。
