RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensionality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
Code (1)
Tasks
Ensemble LearningSimilar Papers 제목 키워드 기반
Deep interpretable ensembles
Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually black box neural networks, or recently,…
Uncertainty QuantificationBorn-Again Tree Ensembles
The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better co…
BIG-bench Machine LearningInterpretable Machine LearningEnsembles of Random Sphere Cover Classifiers
We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to …
AttributeTree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we pro…
Prototype SelectionInterpretable Representation Learning for Additive Rule Ensembles
Small additive ensembles of symbolic rules offer interpretable prediction models. Traditionally, these ensembles use rule conditions based on conjunctions of simple threshold propositions $x \geq t$ on a single input var…
Representation Learning