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Found 3 papers, 3 papers with code
A Voting Approach for Explainable Classification with Rule Learning
State-of-the-art results in typical classification tasks are mostly achieved by unexplainable machine learning methods, like deep neural networks, for instance.
Rule Learning by Modularity
In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable.
Cluster-Based Autoencoders for Volumetric Point Clouds
Autoencoders allow to reconstruct a given input from a small set of parameters.
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