In complex data with high dimensions, the dimension reduction methods are used to increase accuracy and speed in the classification algorithms. Feature clustering methods have had a good performance in the selection of important features of data due to using clustering methods. The process of selecting important features of data is a challenge in feature clustering methods which has led to the creation of different algorithms with different performances. The combination of the clustering methods and metaheuristic algorithms, especially the kind of population-based algorithms, have had good results in most cases. In this paper, a new feature clustering method is proposed which is used as a dimension reduction in the classification of brain tumors in 900 magnetic resonance images (MRI). The classification algorithm includes three main steps: in the first step, the Google-Net and ResNet-18 methods have been used for feature extraction of MRI images. Due to the creation of many features using the Google-Net and ResNet-18 methods, a new proposed feature clustering is introduced to reduce the feature dimensions in the second step. In designing the feature clustering algorithm, a new metaheuristic algorithm is introduced which is called the crocodiles hunting strategy optimization algorithm (CHS) that simulates crocodiles’ behavior in hunting. Also, the feature clustering algorithm introduced the new chromosome encoding for feature clustering which is called feature clustering based on the crocodiles hunting strategy optimization algorithm (FC-CHS). Finally, in the third step, the support vector machine (SVM) algorithm is used for classification. According to the results of classification on the MRI images, the proposed algorithm has achieved high accuracy in Google-Net and ResNet features based on confusion matrices. For comparing the performance of the FC-CHS, this algorithm is compared with five well-known dimension reduction algorithms. Also, real data are used to further investigate the performance of the FC-CHS algorithm. The results show that the combination of the FC-CHS and SVM algorithms have been reached high accuracy in Iris, and Wine data, and in other real data, the proposed algorithm is outperformed compared to other dimension reduction methods in most cases.

中文翻译：

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一种基于鳄鱼狩猎策略优化算法的MRI图像分类特征聚类新方法

在高维的复杂数据中，降维方法用于提高分类算法的准确性和速度。由于使用了聚类方法，特征聚类方法在选择数据的重要特征方面有很好的表现。选择数据的重要特征的过程是特征聚类方法中的一个挑战，这导致了具有不同性能的不同算法的创建。聚类方法和元启发式算法的结合，尤其是基于群体的算法，在大多数情况下都取得了很好的效果。在本文中，提出了一种新的特征聚类方法，将其用作 900 幅磁共振图像 (MRI) 中脑肿瘤分类中的降维。分类算法包括三个主要步骤：在第一步中，Google-Net 和 ResNet-18 方法已用于 MRI 图像的特征提取。由于使用 Google-Net 和 ResNet-18 方法创建了许多特征，因此在第二步中引入了新提出的特征聚类来减少特征维度。在特征聚类算法的设计中，引入了一种新的元启发式算法，称为鳄鱼狩猎策略优化算法（CHS），它模拟鳄鱼在狩猎中的行为。此外，特征聚类算法引入了新的染色体编码进行特征聚类，称为基于鳄鱼狩猎策略优化算法（FC-CHS）的特征聚类。最后，在第三步中，使用支持向量机（SVM）算法进行分类。根据对MRI图像的分类结果，该算法在基于混淆矩阵的Google-Net和ResNet特征上均取得了较高的准确率。为了比较 FC-CHS 的性能，将该算法与五种著名的降维算法进行了比较。此外，真实数据用于进一步研究 FC-CHS 算法的性能。结果表明，FC-CHS 和 SVM 算法的结合在 Iris 和 Wine 数据上已经达到了很高的准确率，在其他真实数据中，该算法在大多数情况下优于其他降维方法。该算法与五种著名的降维算法进行了比较。此外，真实数据用于进一步研究 FC-CHS 算法的性能。结果表明，FC-CHS 和 SVM 算法的结合在 Iris 和 Wine 数据上已经达到了很高的准确率，在其他真实数据中，该算法在大多数情况下优于其他降维方法。该算法与五种著名的降维算法进行了比较。此外，真实数据用于进一步研究 FC-CHS 算法的性能。结果表明，FC-CHS 和 SVM 算法的结合在 Iris 和 Wine 数据上已经达到了很高的准确率，在其他真实数据中，该算法在大多数情况下优于其他降维方法。