A precise and stable machine learning algorithm: eigenvalue classification (EigenClass)

Abstract

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.