Interpretable Image Classification Using Sparse Oblique Decision Trees
Interpretable Image Classification Using Sparse Oblique Decision Trees
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DOI：
10.1109/icassp43922.2022.9747873
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Suryabhan Singh Hada;Miguel Á. Carreira-Perpiñán
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文献类型：
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作者：
Suryabhan Singh Hada;Miguel Á. Carreira-Perpiñán
解释图像数据集是一项艰巨的任务，因为每个图像都包含大量不相关的数据。本文提出了一种简单而有效的方法来解释图像数据集。我们通过使用稀疏斜树作为从数据集中选择特征的工具来实现这一点。这些树不仅准确，而且非常可解释。树的层次结构有助于可视化数据集中的底层模式。通过研究节点的权重，我们可以确定哪些特征集可以区分类或类组。我们有效地证明了我们的结果在多个图像数据集。
Interpreting the image datasets is a difficult task, as each image contains a lot of irrelevant data. This paper presents a simple yet effective method to interpret the image datasets. We achieve this by using sparse oblique trees as a tool to select features from the dataset. These trees are not only accurate but also very interpretable. The hierarchical structure of the tree helps to visualize the underlying patterns in the dataset. By studying the weights of the nodes, we can determine what set of features differentiate between classes or groups of classes. We effectively demonstrate our results in multiple image datasets.