In this study, a precise and efficient eigenvalue-based machine learning algorithm, particularly denoted as Eigenvalue Classification (EigenClass) algorithm, has been presented to deal with classification problems. The EigenClass algorithm is constructed by exploiting an eigenvalue-based proximity evaluation. To appreciate the classification performance of EigenClass, it is compared with the well-known algorithms, such as k-nearest neighbours, fuzzy k-nearest neighbours, random forest (RF) and multi-support vector machines. Number of 20 different datasets with various attributes and classes are used for the comparison. Every algorithm is trained and tested for 30 runs through 5-fold cross-validation. The results are then compared among each other in terms of the most used measures, such as accuracy, precision, recall, micro-F-measure, and macro-F-measure. It is demonstrated that EigenClass exhibits the best classification performance for 15 datasets in terms of every metric and, in a pairwise comparison, outperforms the other algorithms for at least 16 datasets in consideration of each metric. Moreover, the algorithms are also compared through statistical analysis and computational complexity. Therefore, the achieved results show that EigenClass is a precise and stable algorithm as well as the most successful algorithm considering the overall classification performances.

在这项研究中，提出了一种精确高效的基于特征值的机器学习算法，特别是表示为特征值分类（EigenClass）算法，以解决分类问题。通过利用基于特征值的邻近度评估来构造EigenClass算法。为了了解EigenClass的分类性能，将其与众所周知的算法进行了比较，例如k最近邻，模糊k最近邻，随机森林（RF）和多支持向量机。比较使用具有不同属性和类别的20个不同数据集的数量。通过5倍交叉验证，每种算法都经过30次训练和测试。然后根据最常用的度量标准对结果进行比较，例如准确性，精度，召回率，微型F度量，和宏观F测度。事实证明，就每个指标而言，EigenClass表现出针对15个数据集的最佳分类性能，并且考虑到每个指标，在成对比较中，对于至少16个数据集，其性能优于其他算法。此外，还通过统计分析和计算复杂度来比较算法。因此，取得的结果表明，考虑到整体分类性能，EigenClass是一种精确且稳定的算法，也是最成功的算法。还通过统计分析和计算复杂度对算法进行比较。因此，获得的结果表明，考虑到整体分类性能，EigenClass是一种精确且稳定的算法，也是最成功的算法。还通过统计分析和计算复杂度对算法进行比较。因此，取得的结果表明，考虑到整体分类性能，EigenClass是一种精确且稳定的算法，也是最成功的算法。