Developing Explainable Machine Learning Model using Augmented Concept Activation Vector
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929648682663936 |
|---|---|
| author | Hassanpour, Reza Oztoprak, Kasim Netten, Niels Busker, Tony Bargh, Mortaza S. Choenni, Sunil Kizildag, Beyza Kilinc, Leyla Sena |
| author_facet | Hassanpour, Reza Oztoprak, Kasim Netten, Niels Busker, Tony Bargh, Mortaza S. Choenni, Sunil Kizildag, Beyza Kilinc, Leyla Sena |
| contents | Machine learning models use high dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made by these models. We propose a method for measuring the correlation between high-level concepts and the decisions made by a machine learning model. Our method can isolate the impact of a given high-level concept and accurately measure it quantitatively. Additionally, this study aims to determine the prevalence of frequent patterns in machine learning models, which often occur in imbalanced datasets. We have successfully applied the proposed method to fundus images and managed to quantitatively measure the impact of radiomic patterns on the model decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19208 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Developing Explainable Machine Learning Model using Augmented Concept Activation Vector Hassanpour, Reza Oztoprak, Kasim Netten, Niels Busker, Tony Bargh, Mortaza S. Choenni, Sunil Kizildag, Beyza Kilinc, Leyla Sena Machine Learning 68T07 I.2.6 Machine learning models use high dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made by these models. We propose a method for measuring the correlation between high-level concepts and the decisions made by a machine learning model. Our method can isolate the impact of a given high-level concept and accurately measure it quantitatively. Additionally, this study aims to determine the prevalence of frequent patterns in machine learning models, which often occur in imbalanced datasets. We have successfully applied the proposed method to fundus images and managed to quantitatively measure the impact of radiomic patterns on the model decisions. |
| title | Developing Explainable Machine Learning Model using Augmented Concept Activation Vector |
| topic | Machine Learning 68T07 I.2.6 |
| url | https://arxiv.org/abs/2412.19208 |