# This part looks alike this: identifying important parts of explained instances and prototypes

arXiv: https://arxiv.org/abs/2505.05597 · HTML: https://mlsanity.com/paper/2505.05597/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions](https://mlsanity.com/paper/1710.04806/) (2017)
- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [A-PETE: Adaptive Prototype Explanations of Tree Ensembles](https://mlsanity.com/paper/2405.21036/) (2024)
- [Semantic Prototypes: Enhancing Transparency Without Black Boxes](https://mlsanity.com/paper/2407.15871/) (2024)

## Built on by (most cited first, 2 of 2)

- [An interpretable prototype parts-based neural network for medical tabular data](https://mlsanity.com/paper/2603.05423/) (2026)
- [Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations](https://mlsanity.com/paper/2605.21646/) (2026)

## Abstract (arXiv)

Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. We propose a novel approach to identify the most informative features within prototypes, termed alike parts. Using feature importance scores derived from an agnostic explanation method, it emphasizes the most relevant overlapping features between an instance and its nearest prototype. Furthermore, the feature importance score is incorporated into the objective function of the prototype selection algorithms to promote global prototypes diversity. Through experiments on six benchmark datasets, we demonstrate that the proposed approach improves user comprehension while maintaining or even increasing predictive accuracy.