In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We model dependency structures among features using a family of chordal graphs (the Triangulated Maximally Filtered Graph), and we maximise the likelihood of features' relevance by studying their relative position inside the network. Such an approach presents three aspects that are particularly satisfactory compared to its alternatives: (i) it is highly tunable and easily adaptable to the nature of input data; (ii) it is fully explainable, maintaining, at the same time, a remarkable level of simplicity; (iii) it is computationally cheaper compared to its alternatives. We test our algorithm on 16 benchmark datasets from different applicative domains showing that it outperforms or matches the current state-of-the-art under heterogeneous evaluation conditions.

翻译：在本文中,我们引入了一种新颖的、不受监督的、基于图表的筛选特征选择技术,它利用了受地形限制的网络代表的力量。我们用一个圆形图组(三角最大过滤图组)来模拟不同特征之间的依赖结构,我们通过研究这些特征在网络中的相对位置来最大限度地提高这些特征的相关性。这种方法提出了与其替代方法相比特别令人满意的三个方面:(一) 它具有高度的金枪鱼特性,并且很容易适应输入数据的性质;(二) 它完全可以解释,同时保持显著的简单程度;(三) 与其替代方法相比,它计算成本更低。 我们用16个来自不同实用领域的基准数据集测试我们的算法,显示它超越或符合不同评价条件下的当前最新数据。