The Scientific and Technological Research Council of Turkey, TUBITAK, Ankara, Turkey.
Department of Computer Engineering, Ankara Medipol University, Ankara, Turkey.
Sci Rep. 2022 Sep 8;12(1):15210. doi: 10.1038/s41598-022-19419-7.
We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which efficiently works with time-series numerical datasets. In this study, we have extended SNAc to work with any type of tabular data by showing its classification efficiency on a broader collection of datasets that may contain numerical and categorical features. This version of GSNAc simply works by transforming traditional tabular data into a network where samples of the tabular dataset are represented as nodes and similarities between the samples are reflected as edges connecting the corresponding nodes. The raw network graph is further simplified and enriched by its edge space to extract a visualizable 'graph classifier model-GCM'. The concept of the GSNAc classification model relies on the study of node similarities over network graphs. In the prediction step, the GSNAc model maps test nodes into GCM, and evaluates their average similarity to classes by employing vectorial and topological metrics. The novel side of this research lies in transforming multidimensional data into a 2D visualizable domain. This is realized by converting a conventional dataset into a network of 'samples' and predicting classes after a careful and detailed network analysis. We exhibit the classification performance of GSNAc as an effective classifier by comparing it with several well-established machine learning classifiers using some popular benchmark datasets. GSNAc has demonstrated superior or comparable performance compared to other classifiers. Additionally, it introduces a visually comprehensible process for the benefit of end-users. As a result, the spin-off contribution of GSNAc lies in the interpretability of the prediction task since the process is human-comprehensible; and it is highly visual.
我们提出了一种新的基于图论和社交网络分析技术的监督视觉机器学习分类器 GSNAc。在之前的研究中，我们使用了社交网络分析技术并引入了一种新的分类模型（称为基于社交网络分析的分类器 SNAc），该模型可以有效地处理时间序列数值数据集。在本研究中，我们通过在更广泛的数据集集合上展示其分类效率，将 SNAc 扩展到可用于任何类型的表格数据。这个版本的 GSNAc 通过将传统的表格数据转换为网络来工作，其中表格数据集的样本表示为节点，并且样本之间的相似性反映为连接相应节点的边。原始网络图通过其边空间进一步简化和丰富，以提取可可视化的“图形分类器模型-GCM”。GSNAc 分类模型的概念依赖于网络图上节点相似性的研究。在预测步骤中，GSNAc 模型将测试节点映射到 GCM，并通过使用向量和拓扑度量来评估它们与类别的平均相似性。这项研究的新颖之处在于将多维数据转换为 2D 可可视化的领域。这是通过将常规数据集转换为“样本”网络并在仔细和详细的网络分析后进行分类来实现的。我们通过使用一些流行的基准数据集将 GSNAc 与几个成熟的机器学习分类器进行比较，展示了 GSNAc 作为有效分类器的分类性能。GSNAc 与其他分类器相比表现出优越或相当的性能。此外，它为最终用户引入了一个可理解的可视化过程。因此，GSNAc 的衍生贡献在于预测任务的可解释性，因为该过程是人类可理解的，并且具有高度可视化。
