# Visualisation and knowledge discovery from interpretable models

arXiv: https://arxiv.org/abs/2005.03632 · HTML: https://mlsanity.com/paper/2005.03632/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 6 of 6)

- [Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective](https://mlsanity.com/paper/2401.04374/) (2024)
- [Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets](https://mlsanity.com/paper/2206.02056/) (2022)
- [Generalized Relevance Learning Grassmann Quantization](https://mlsanity.com/paper/2403.09183/) (2024)
- [A prototype-based model for set classification](https://mlsanity.com/paper/2408.13720/) (2024)
- [Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces](https://mlsanity.com/paper/2401.12842/) (2024)
- [An explainable approach to detect case law on housing and eviction issues within the HUDOC database](https://mlsanity.com/paper/2410.02978/) (2024)

## Abstract (arXiv)

Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem. These models are also capable of visualisation of the classifier and decision boundaries: they are the angle based variants of Learning Vector Quantization. We have demonstrated the algorithms on a synthetic dataset and a real-world one (heart disease dataset from the UCI repository). The newly developed classifiers helped in investigating the complexities of the UCI dataset as a multiclass problem. The performance of the developed classifiers were comparable to those reported in literature for this dataset, with additional value of interpretability, when the dataset was treated as a binary class problem.