Interpretable Image Classification Using Sparse Oblique Decision Trees

Suryabhan Singh Hada; Miguel Á. Carreira-Perpiñán

ICASSP 2022accepted0 citations

Interpretable Image Classification Using Sparse Oblique Decision Trees

Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán

Abstract

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.

BibTeX

@inproceedings{icassp2022_interpretableima,
  title = {Interpretable Image Classification Using Sparse Oblique Decision Trees},
  author = {Suryabhan Singh Hada and Miguel Á. Carreira-Perpiñán},
  booktitle = {ICASSP 2022},
  year = {2022}
}