Su Yuebin, Guo Jin
School of Information Science and Technology, Southwest Jiao Tong University, Chengdu 610031, China.
School of Mathematics and Statistics, Sichuan University of Science & Engineering, Zigong 643000, China.
Comput Intell Neurosci. 2017;2017:6573623. doi: 10.1155/2017/6573623. Epub 2017 Aug 15.
For data mining, reducing the unnecessary redundant attributes which was known as attribute reduction (AR), in particular, reducts with minimal cardinality, is an important preprocessing step. In the paper, by a coding method of combination subset of attributes set, a novel search strategy for minimal attribute reduction based on rough set theory (RST) and fish swarm algorithm (FSA) is proposed. The method identifies the core attributes by discernibility matrix firstly and all the subsets of noncore attribute sets with the same cardinality were encoded into integers as the individuals of FSA. Then, the evolutionary direction of the individual is limited to a certain extent by the coding method. The fitness function of an individual is defined based on the attribute dependency of RST, and FSA was used to find the optimal set of reducts. In each loop, if the maximum attribute dependency and the attribute dependency of condition attribute set are equal, then the algorithm terminates, otherwise adding a single attribute to the next loop. Some well-known datasets from UCI were selected to verify this method. The experimental results show that the proposed method searches the minimal attribute reduction set effectively and it has the excellent global search ability.
对于数据挖掘而言，减少被称为属性约简（AR）的不必要冗余属性，尤其是具有最小基数的约简，是一个重要的预处理步骤。在本文中，通过一种属性集组合子集的编码方法，提出了一种基于粗糙集理论（RST）和鱼群算法（FSA）的新颖的最小属性约简搜索策略。该方法首先通过区分矩阵识别核心属性，并且将所有具有相同基数的非核心属性集的子集编码为整数作为鱼群算法的个体。然后，通过编码方法在一定程度上限制个体的进化方向。基于粗糙集理论的属性依赖性定义个体的适应度函数，并使用鱼群算法找到约简的最优集。在每个循环中，如果最大属性依赖性与条件属性集的属性依赖性相等，则算法终止，否则在下一个循环中添加单个属性。选择了一些来自UCI的著名数据集来验证该方法。实验结果表明，所提出的方法能够有效地搜索最小属性约简集，并且具有出色的全局搜索能力。
