ArticleIEEE transactions on nanobioscience2012
Combined rule extraction and feature elimination in supervised classification.
Article in IEEE transactions on nanobioscience, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Predicting the risk of primary Sjögren's syndrome with key N7-methylguanosine-related genes: A novel XGBoost model.Heliyon · 2024Article
- PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases.Frontiers in artificial intelligence · 2020Article
- Learning accurate and interpretable models based on regularized random forests regression.BMC systems biology · 2014Article
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Authors and funding
8 authors.
Funding
Abstract
There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.
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Registered trials
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