Binary grey wolf optimization approaches for feature selection
Binary grey wolf optimization approaches for feature selection
复制标题
DOI：
10.1016/j.neucom.2015.06.083
复制
发表时间：
2016-01-08
期刊：
影响因子：
6
通讯作者：
Hassanien, Aboul Ella
中科院分区：
文献类型：
--
作者：
Emary, E.;Zawba, Hossam M.;Hassanien, Aboul Ella
在这项工作中，提出了灰狼优化（GWO）的一种新颖的二进制版本，并用于选择用于分类目的的最佳特征子集。灰狼优化器（GWO）是最新的仿生优化技术之一，它模拟自然界中灰狼的狩猎过程。这里介绍的二进制版本是使用两种不同的方法执行的。在第一种方法中，将前三个最佳解决方案的各个步骤二值化，然后在三个基本动作之间执行随机交叉以找到更新的二值灰狼位置。在第二种方法中，使用sigmoidal函数来压缩连续更新的位置，然后对这些值进行随机阈值以找到更新的二元灰狼位置。在特征选择域中采用了两种二元灰狼优化（bGWO）方法来寻找特征子集，从而最大化分类精度，同时最小化所选特征的数量。所提出的二进制版本与该领域使用的两种常见优化器（即粒子群优化器和遗传算法）进行了比较。一组评估指标用于评估和比较 UCI 存储库中 18 个不同数据集的不同方法。结果证明了所提出的灰狼优化（bGWO）的二进制版本能够在特征空间中搜索最佳特征组合，而不管初始化和使用的随机算子如何。 (C) 2015 Elsevier B.V. 保留所有权利。
In this work, a novel binary version of the grey wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the latest bio-inspired optimization techniques, which simulate the hunting process of grey wolves in nature. The binary version introduced here is performed using two different approaches. In the first approach, individual steps toward the first three best solutions are binarized and then stochastic crossover is performed among the three basic moves to find the updated binary grey wolf position. In the second approach, sigmoidal function is used to squash the continuous updated position, then stochastically threshold these values to find the updated binary grey wolf position. The two approach for binary grey wolf optimization (bGWO) are hired in the feature selection domain for finding feature subset maximizing the classification accuracy while minimizing the number of selected features. The proposed binary versions were compared to two of the common optimizers used in this domain namely particle swarm optimizer and genetic algorithms. A set of assessment indicators are used to evaluate and compared the different methods over 18 different datasets from the UCI repository. Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators. (C) 2015 Elsevier B.V. All rights reserved.