Are More Features Better? A Response to Attributes Reduction Using Fuzzy Rough Sets
Are More Features Better? A Response to Attributes Reduction Using Fuzzy Rough Sets
复制标题
DOI：
10.1109/tfuzz.2009.2026639
复制
发表时间：
2009-12
影响因子：
11.9
通讯作者：
Richard Jensen;Q. Shen
中科院分区：
文献类型：
--
作者：
Richard Jensen;Q. Shen
最近的一篇《TRANSACTIONS ON FUZZY SYSTEMS》论文提出了一种新的模糊粗糙特征选择器（FRFS），声称数据集中保留的属性越多，近似值和由此产生的模型就越好。 [Tsang，IEEE 翻译。模糊系统，卷。 16、没有。 5，第 1130-1141 页]。这一主张已被用作对原始 FRFS 方法的主要批评 [Jensen 和 Shen，IEEE Trans。模糊系统，卷。 15、没有。 1，第 73-89 页，2007 年 2 月]。尽管在某些应用中，可能需要考虑尽可能多的特征，但该主张与特征选择背后的动机相反，涉及维数灾难、冗余和不相关特征的存在，以及大量记录数据缩减后建模技术观察到的改进的文献。这封信讨论了这个问题，以及 Tsang 提出的另外两个问题 [IEEE Trans.模糊系统，卷。 16、没有。 5，第 1130-1141 页，2008 年 10 月]关于原始算法。
A recent TRANSACTIONS ON FUZZY SYSTEMS paper proposing a new fuzzy-rough feature selector (FRFS) has claimed that the more attributes remain in datasets, the better the approximations and hence resulting models. [Tsang , IEEE Trans. Fuzzy Syst. , vol. 16, no. 5, pp. 1130-1141]. This claim has been used as a primary criticism of the original FRFS method [Jensen and Shen, IEEE Trans. Fuzzy Syst., vol. 15, no. 1, pp. 73-89, Feb. 2007]. Although, in certain applications, it may be necessary to consider as many features as possible, the claim is contrary to the motivation behind feature selection concerning the curse of dimensionality, the presence of redundant and irrelevant features, and the large amount of literature documenting observed improvements in modeling techniques following data reduction. This letter discusses this issue, as well as two other issues raised by Tsang [IEEE Trans. Fuzzy Syst., vol. 16, no. 5, pp. 1130-1141, Oct. 2008] regarding the original algorithm.