Multi-Objective Symbolic Regression for Data-Driven Scoring System Management
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- @InProceedings{Ferrari:2022:ICDM,
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author = "Davide Ferrari and Veronica Guidetti and
Federica Mandreoli",
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booktitle = "2022 IEEE International Conference on Data Mining
(ICDM)",
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title = "Multi-Objective Symbolic Regression for Data-Driven
Scoring System Management",
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year = "2022",
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pages = "945--950",
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abstract = "Scores are mathematical combinations of elementary
indicators (EIs) widely used to measure complex
phenomena. Upon the theoretical framework definition,
score construction requires a method to aggregate EIs.
Aggregation is usually chosen among known methodologies
fixing its shape through a try and error approach. Only
then are the predictive power, the distribution of the
index, and its ability to stratify the population
measured. In this paper, we propose a novel data-driven
approach that generates analytic aggregation methods
relying on multi-objective symbolic regression. We
translate the properties that the index must exhibit
into optimization goals so that optimal index
candidates replicate target variables, data balancing,
and stratification. We run experiments on real data
sets to solve three main score management problems:
data-driven score simplification, generation, and
combination. The results obtained show the
effectiveness and robustness of the proposed
approach.",
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keywords = "genetic algorithms, genetic programming, Power
measurement, Shape, Aggregates, Sociology, Robustness,
Indexes, Data mining, scoring systems, multi-objective
symbolic regression",
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DOI = "
doi:10.1109/ICDM54844.2022.00112",
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ISSN = "2374-8486",
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month = nov,
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notes = "Also known as \cite{10027776}",
- }
Genetic Programming entries for
Davide Ferrari
Veronica Guidetti
Federica Mandreoli
Citations