Improving Classifier Performance with XAI Methods Framework

Conceitos essenciais

Using XAI methods to enhance pre-trained DL classifiers' performance.

Resumo

The paper introduces a framework to automatically enhance the performance of pre-trained Deep Learning (DL) classifiers using eXplainable Artificial Intelligence (XAI) methods. It aims to bridge the gap between XAI and model performance enhancement by integrating explanations with classifier outputs. Two learning strategies, auto-encoder-based and encoder-decoder-based, are outlined for this purpose. The proposed approach focuses on improving already trained ML models without extensive retraining, leveraging XAI techniques effectively.

Estatísticas

"arXiv:2403.10373v1 [cs.LG] 15 Mar 2024"

Citações

"A notable aspect of our approach is that we can directly obtain the explanation of the model responses without using an XAI method."
"It would be valuable to investigate the impact of the proposed approach on the automatic improvement of an already trained ML model."
"Investigating the robustness of our approach with respect to a modification of the original dataset used to train the model M is certainly a relevant aspect."

Principais Insights Extraídos De

Towards a general framework for improving the performance of classifiers using XAI methods

by Andr... às arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10373.pdf

Perguntas Mais Profundas

How can integrating explanations directly into model predictions impact overall AI system performance

Integrating explanations directly into model predictions can have a significant impact on overall AI system performance by enhancing transparency, interpretability, and trustworthiness. By providing insights into the decision-making process of AI models, stakeholders can better understand why specific outcomes are generated. This transparency not only aids in building user trust but also enables domain experts to validate the model's decisions and identify potential biases or errors. Moreover, integrating explanations can lead to improved model robustness by highlighting areas where the model may be making incorrect predictions or relying on irrelevant features. This feedback loop allows for continuous refinement and optimization of the AI system, ultimately boosting its performance.

What potential challenges or limitations might arise when implementing XAI methods for enhancing pre-trained classifiers

Implementing eXplainable Artificial Intelligence (XAI) methods for enhancing pre-trained classifiers may present several challenges and limitations. One key challenge is ensuring that the XAI techniques used provide accurate and meaningful explanations that align with human intuition. If the explanations generated are complex or difficult to interpret, they may not effectively contribute to improving classifier performance. Additionally, integrating XAI methods into existing models without extensive retraining requires careful consideration of how these techniques will interact with the pre-existing architecture. Ensuring compatibility between XAI methods and different types of classifiers can also be a limitation as certain approaches may be more suitable for specific models than others. Furthermore, scalability issues could arise when applying XAI techniques to large-scale datasets or complex deep learning architectures due to computational constraints.

How could exploring different datasets and architectures influence the effectiveness of this proposed framework

Exploring different datasets and architectures can significantly influence the effectiveness of the proposed framework for automatically improving pre-trained classifiers using XAI methods. Utilizing diverse datasets allows researchers to assess how well the framework generalizes across various domains and data distributions, providing insights into its robustness and adaptability. Different architectures offer opportunities to evaluate which design choices work best with specific XAI techniques and how they impact overall model performance enhancement. By experimenting with a range of datasets and architectures, researchers can gain a deeper understanding of how external factors influence the framework's efficacy in different contexts, leading to more informed decisions regarding its implementation in real-world applications.