Positive approximation: An accelerator for attribute reduction in rough set theory
Positive approximation: An accelerator for attribute reduction in rough set theory
复制标题
正近似：粗糙集理论中属性约简的加速器
DOI：
10.1016/j.artint.2010.04.018
复制
发表时间：
2010-06-01
影响因子：
14.4
通讯作者：
Dang, Chuangyin
中科院分区：
文献类型：
--
作者：
Qian, Yuhua;Liang, Jiye;Dang, Chuangyin
在模式识别、机器学习和数据挖掘等领域，特征选择是一个具有挑战性的问题。考虑到粗糙集理论中引入的一致性度量，特征选择问题，也称为属性约简，旨在保留原始特征的区分能力。许多启发式属性约简算法已经被提出，然而，这些方法往往是计算耗时。为了克服这一缺点，我们引入了一个理论框架的基础上粗糙集理论，称为积极的近似，它可以用来加速属性约简的启发式过程。基于该加速器，设计了一种通用的属性约简算法.通过加速器的使用，对粗糙集理论中几种有代表性的启发式属性约简算法进行了改进。注意，每个修改后的算法可以选择相同的属性约简作为其原始版本，因此具有相同的分类精度。实验结果表明，这些改进的算法优于原来的同类算法。值得注意的是，当处理较大的数据集时，修改后的算法的性能变得更加明显。(C)2010 Elsevier B.V.保留所有权利。
Feature selection is a challenging problem in areas such as pattern recognition, machine learning and data mining. Considering a consistency measure introduced in rough set theory, the problem of feature selection, also called attribute reduction, aims to retain the discriminatory power of original features. Many heuristic attribute reduction algorithms have been proposed however, quite often, these methods are computationally time-consuming. To overcome this shortcoming, we introduce a theoretic framework based on rough set theory, called positive approximation, which can be used to accelerate a heuristic process of attribute reduction. Based on the proposed accelerator, a general attribute reduction algorithm is designed. Through the use of the accelerator, several representative heuristic attribute reduction algorithms in rough set theory have been enhanced. Note that each of the modified algorithms can choose the same attribute reduct as its original version, and hence possesses the same classification accuracy. Experiments show that these modified algorithms outperform their original counterparts. It is worth noting that the performance of the modified algorithms becomes more visible when dealing with larger data sets. (C) 2010 Elsevier B.V. All rights reserved.