论文标题
使用鲸鱼优化算法的可扩展特征选择和意见矿工
A Scalable Feature Selection and Opinion Miner Using Whale Optimization Algorithm
论文作者
论文摘要
由于近年来文本文档和评论的快速增长，当前的分析技术不足以满足用户的需求。使用特征选择技术不仅支持更好地了解数据，还可以提高速度和准确性。在本文中，考虑了鲸鱼优化算法并将其应用于搜索特征的最佳子集。众所周知，F-Measure是基于精度和回忆的度量，在比较分类器时非常受欢迎。为了评估和比较实验结果，将部分，随机树，随机森林和RBF网络分类算法应用于不同数量的特征。实验结果表明，随机森林在500个特征上具有最佳准确性。关键字：特征选择，鲸鱼优化算法，选择最佳，分类算法
Due to the fast-growing volume of text documents and reviews in recent years, current analyzing techniques are not competent enough to meet the users' needs. Using feature selection techniques not only support to understand data better but also lead to higher speed and also accuracy. In this article, the Whale Optimization algorithm is considered and applied to the search for the optimum subset of features. As known, F-measure is a metric based on precision and recall that is very popular in comparing classifiers. For the evaluation and comparison of the experimental results, PART, random tree, random forest, and RBF network classification algorithms have been applied to the different number of features. Experimental results show that the random forest has the best accuracy on 500 features. Keywords: Feature selection, Whale Optimization algorithm, Selecting optimal, Classification algorithm
