A novel evaluation methodology for supervised Feature Ranking algorithms
This addresses a gap in feature selection and interpretable AI by providing a consistent evaluation approach, though it is incremental as it builds on existing methods without introducing new algorithms.
The paper tackles the lack of systematic evaluation for feature ranking algorithms by proposing a new methodology using synthetic datasets with known feature importance scores, resulting in an open-source benchmarking framework called fseval that enables large-scale experiments and interactive analysis.
Both in the domains of Feature Selection and Interpretable AI, there exists a desire to `rank' features based on their importance. Such feature importance rankings can then be used to either: (1) reduce the dataset size or (2) interpret the Machine Learning model. In the literature, however, such Feature Rankers are not evaluated in a systematic, consistent way. Many papers have a different way of arguing which feature importance ranker works best. This paper fills this gap, by proposing a new evaluation methodology. By making use of synthetic datasets, feature importance scores can be known beforehand, allowing more systematic evaluation. To facilitate large-scale experimentation using the new methodology, a benchmarking framework was built in Python, called fseval. The framework allows running experiments in parallel and distributed over machines on HPC systems. By integrating with an online platform called Weights and Biases, charts can be interactively explored on a live dashboard. The software was released as open-source software, and is published as a package on the PyPi platform. The research concludes by exploring one such large-scale experiment, to find the strengths and weaknesses of the participating algorithms, on many fronts.