Visualisation and knowledge discovery from interpretable models
This work addresses the problem of model transparency and fairness in decision-making for sectors affecting human lives, though it is incremental as it builds on existing Learning Vector Quantization methods.
The authors tackled the need for interpretable machine learning models by introducing angle-based variants of Learning Vector Quantization that handle missing values and enable visualization, achieving performance comparable to existing methods on a heart disease dataset while adding interpretability.
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.