首页|Researchers at University of Limoges Report New Data on Machine@@Learning (Evolut ionary-based Ensemble Feature Selection Technique@@for Dynamic Application-specif ic Credit Risk Optimization In@@Fintech Lending)
Researchers at University of Limoges Report New Data on Machine@@Learning (Evolut ionary-based Ensemble Feature Selection Technique@@for Dynamic Application-specif ic Credit Risk Optimization In@@Fintech Lending)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Fresh data on Machine Learning are presented in a new report. According to news reporting out ofLimoges, France, by NewsRx edito rs, the research stated, “This study introduces EFSGA, an evolutionarybasedens emble learning and feature selection technique inspired by the genetic algorithm , tailored asan optimized application-specific credit classifier for dynamic de fault prediction in FinTech lending. Ourapproach addresses existing gaps in met aheuristic applications for credit risk optimization by (i) hybridizingmetaheur istics with machine learning to accommodate the dynamic nature of time-evolving systems anduncertainty, (ii) leveraging distributed and parallel computing for real-time solutions in complex risk decisionprocesses, and (iii) enhancing appl icability to unbalanced learning scenarios.”
LimogesFranceEuropeAlgorithmsCyb orgsEmerging TechnologiesGenetic AlgorithmsMachine LearningUniversity of Limoges
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