论文标题
Atom搜索优化使用模拟退火 - 一种混合元启发式方法用于特征选择
Atom Search Optimization with Simulated Annealing -- a Hybrid Metaheuristic Approach for Feature Selection
论文作者
论文摘要
“混合荟萃术”是优化和特征选择领域最有趣的趋势之一（FS）。在本文中，我们提出了一种原子搜索优化（ASO）的二进制变体及其与模拟退火的混合物，称为FS的ASO-SA技术。为了将ASO使用的实际值映射到FS的二进制域，我们使用了两个不同的传输函数：S形和V形。我们已经将这种技术与称为“ SA”的本地搜索技术杂交了，我们已经在4个不同类别的25个数据集上应用了拟议的特征选择方法：UCI，手写数字识别，文本，非文本，非文本分离和面部情感识别。我们已经使用了3个不同的分类器（K-Nearest邻域，多层感知器和随机森林）来评估二进制ASO，ASO-SA所选所选的强度，并将结果与一些最近基于包装的算法进行了比较。实验结果证实了所提出的方法在分类精度和选定特征的数量方面的优势。
'Hybrid meta-heuristics' is one of the most interesting recent trends in the field of optimization and feature selection (FS). In this paper, we have proposed a binary variant of Atom Search Optimization (ASO) and its hybrid with Simulated Annealing called ASO-SA techniques for FS. In order to map the real values used by ASO to the binary domain of FS, we have used two different transfer functions: S-shaped and V-shaped. We have hybridized this technique with a local search technique called, SA We have applied the proposed feature selection methods on 25 datasets from 4 different categories: UCI, Handwritten digit recognition, Text, non-text separation, and Facial emotion recognition. We have used 3 different classifiers (K-Nearest Neighbor, Multi-Layer Perceptron and Random Forest) for evaluating the strength of the selected featured by the binary ASO, ASO-SA and compared the results with some recent wrapper-based algorithms. The experimental results confirm the superiority of the proposed method both in terms of classification accuracy and number of selected features.
