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A combined entropy-based approach for a proactive credit scoring · Study

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ArticleA combined entropy-based approach for a proactive credit scoring

Salvatore Carta2019

Article2019

A combined entropy-based approach for a proactive credit scoring

Salvatore Carta · Anselmo Ferreira · Diego Reforgiato Recupero · Marco Saia · Roberto Saia

40 citations·0/0 files stored·Engineering Applications of Artificial Intelligence

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Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

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References

2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 1997Statistical Classification Methods in Consumer Credit Scoring: A Review 2010A comparative assessment of ensemble learning for credit scoring 2005A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines 1997What's the point of credit scoring?2005Hybrid mining approach in the design of credit scoring models 2018Dynamic ensemble classification for credit scoring using soft probability 2005Data mining feature selection for credit scoring models

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2022Research on significant factors affecting adoption of blockchain technology for enterprise distributed applications based on integrated MCDM FCEM-MULTIMOORA-FG method 2022Novel hybrid ensemble credit scoring model with stacking-based noise detection and weight assignment 2022Optimal cost-sensitive credit scoring using a new hybrid performance metric 2023Profit- and risk-driven credit scoring under parameter uncertainty: A multiobjective approach 2021

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Year2019

Typearticle

Published2019-10-17

VenueEngineering Applications of Artificial Intelligence

ISSN-L0952-1976

Languageen

Citations40

Files0 public / 0

ShelfFinancial Distress and Bankruptcy Prediction

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doi10.1016/j.engappai.2019.103292

openalex_workW2980795138

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Feature selection based on machine learning for credit scoring : An evaluation of filter and embedded methods