Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition‐based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection.

中文翻译：

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一种采用花授粉算法进行特征选择的新型混合鲸鱼优化算法：案例研究垃圾邮件检测

数据挖掘中的特征选择（FS）是模式识别中最具挑战性和最重要的活动之一。本文基于对立学习（OBL）的概念，命名为HWOAFPA，提出了一种新的鲸鱼优化算法（WOA）和花粉授粉算法（FPA）的混合模型。程序是先运行WOA，然后在运行的同时，OBL会更改WOA数量。并且，为了提高收敛的准确性和速度，将其用作FPA的初始填充。为了评估所提出方法的性能，分两个步骤进行了实验。对UCI数据存储库中的10个数据集和电子邮件垃圾邮件检测数据集进行了实验。从第一步获得的结果表明，与其他基本的元启发式算法相比，该方法在选择的平均大小和分类准确性方面更为成功。此外，第二步的结果表明，在电子邮件垃圾邮件数据集上运行的建议方法在电子邮件垃圾邮件检测的准确性方面比其他类似算法更准确。