Learning customized and optimized lists of rules with mathematical programming
- We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. The flexible framework of mathematical programming allows users to create customized models with a provable guarantee of optimality. The software reviewed as part of this submission was given the DOI (Digital Object Identifier) https://doi.org/10.5281/zenodo.1344142.
Metadaten| Author: | Cynthia Rudin, Şeyda Ertekin |
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| DOI: | https://doi.org/10.1007/s12532-018-0143-8 |
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| ISSN: | 1867-2949 |
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| Parent Title (English): | Mathematical Programming Computation |
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| Publisher: | Springer Science and Business Media LLC |
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| Document Type: | Article |
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| Language: | English |
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| Year of Completion: | 2018 |
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| Tag: | Software; Theoretical Computer Science |
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| Volume: | 10 |
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| Issue: | 4 |
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| Page Number: | 44 |
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| First Page: | 659 |
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| Last Page: | 702 |
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| Mathematical Programming Computation : | MPC 2018 - Issue 4 |
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