On the public citation graph

Feature Selection in a Credit Scoring Model

0 signed · 113 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9781118856178openalex-snapshot
Beyond Basic Statistics
inferredcites →https://doi.org/10.1006/bare.1999.0120openalex-snapshot
THE SENSITIVITY OF PREDICTION MODELS TO THE NON-NORMALITY OF BOUNDED AND UNBOUNDED FINANCIAL RATIOS
inferredcites →https://doi.org/10.1007/978-1-4614-7138-7openalex-snapshot
An Introduction to Statistical Learning
inferredcites →https://doi.org/10.1007/978-3-642-30864-2_25openalex-snapshot
Non-parametric Statistical Analysis of Machine Learning Methods for Credit Scoring
inferredcites →https://doi.org/10.1007/bf00426854openalex-snapshot
A Guide to using the collinearity diagnostics
inferredcites →https://doi.org/10.1007/s00521-019-04537-7openalex-snapshot
Machine learning and decision support system on credit scoring
inferredcites →https://doi.org/10.1016/j.asoc.2020.106263openalex-snapshot
Statistical and machine learning models in credit scoring: A systematic literature survey
inferredcites →https://doi.org/10.1016/j.ejor.2006.04.051openalex-snapshot
Comprehensible credit scoring models using rule extraction from support vector machines
inferredcites →https://doi.org/10.1016/j.ejor.2009.03.036openalex-snapshot
Support vector machines for default prediction of SMEs based on technology credit
inferredcites →https://doi.org/10.1016/j.ejor.2012.04.009openalex-snapshot
An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data
inferredcites →https://doi.org/10.1016/j.ejor.2015.05.030openalex-snapshot
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
inferredcites →https://doi.org/10.1016/j.eswa.2004.08.009openalex-snapshot
An application of support vector machines in bankruptcy prediction model
inferredcites →https://doi.org/10.1016/j.eswa.2005.01.003openalex-snapshot
Building credit scoring models using genetic programming
inferredcites →https://doi.org/10.1016/j.eswa.2007.12.020openalex-snapshot
The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
inferredcites →https://doi.org/10.1016/j.eswa.2008.01.005openalex-snapshot
Support vector machines for credit scoring and discovery of significant features
inferredcites →https://doi.org/10.1016/j.eswa.2008.01.009openalex-snapshot
Support vector machines combined with feature selection for breast cancer diagnosis
inferredcites →https://doi.org/10.1016/j.eswa.2008.06.068openalex-snapshot
A selective ensemble based on expected probabilities for bankruptcy prediction
inferredcites →https://doi.org/10.1016/j.eswa.2008.06.071openalex-snapshot
Accuracy of machine learning models versus “hand crafted” expert systems – A credit scoring case study
inferredcites →https://doi.org/10.1016/j.eswa.2010.04.054openalex-snapshot
Vertical bagging decision trees model for credit scoring
inferredcites →https://doi.org/10.1016/j.eswa.2010.06.048openalex-snapshot
A comparative assessment of ensemble learning for credit scoring
inferredcites →https://doi.org/10.1016/j.eswa.2011.06.028openalex-snapshot
Credit card churn forecasting by logistic regression and decision tree
inferredcites →https://doi.org/10.1016/j.eswa.2013.03.019openalex-snapshot
Consumer credit risk: Individual probability estimates using machine learning
inferredcites →https://doi.org/10.1016/j.eswa.2014.10.037openalex-snapshot
Detecting credit card fraud by Modified Fisher Discriminant Analysis
inferredcites →https://doi.org/10.1016/j.eswa.2015.11.027openalex-snapshot
Business health characterization: A hybrid regression and support vector machine analysis
inferredcites →https://doi.org/10.1016/j.eswa.2016.08.014openalex-snapshot
Data classification with binary response through the Boosting algorithm and logistic regression
inferredcites →https://doi.org/10.1016/j.eswa.2016.12.020openalex-snapshot
A comparative study on base classifiers in ensemble methods for credit scoring
inferredcites →https://doi.org/10.1016/j.eswa.2020.113872openalex-snapshot
A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring
inferredcites →https://doi.org/10.1016/j.frl.2017.06.002openalex-snapshot
Can tree-structured classifiers add value to the investor?
inferredcites →https://doi.org/10.1016/j.is.2011.10.009openalex-snapshot
Cost-sensitive classification with inadequate labeled data
inferredcites →https://doi.org/10.1016/j.jfs.2024.101284openalex-snapshot
How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm
inferredcites →https://doi.org/10.1016/j.jss.2012.05.073openalex-snapshot
Nearest neighbor selection for iteratively kNN imputation
inferredcites →https://doi.org/10.1016/j.patcog.2007.03.014openalex-snapshot
A SVM-based cursive character recognizer
inferredcites →https://doi.org/10.1016/j.procs.2011.04.184openalex-snapshot
Credit Risk Evaluation Model Development Using Support Vector Based Classifiers
inferredcites →https://doi.org/10.1016/s0305-0483(01)00026-3openalex-snapshot
Application of support vector machines in financial time series forecasting
inferredcites →https://doi.org/10.1016/s0378-4266(01)00254-0openalex-snapshot
Bank lending policy, credit scoring and value-at-risk
inferredcites →https://doi.org/10.1016/s0925-2312(03)00372-2openalex-snapshot
Financial time series forecasting using support vector machines
inferredcites →https://doi.org/10.1016/s0933-3657(03)00051-4openalex-snapshot
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
inferredcites →https://doi.org/10.1016/s1088-467x(98)00007-9openalex-snapshot
Data preprocessing and intelligent data analysis
inferredcites →https://doi.org/10.1029/95wr02966openalex-snapshot
A Nearest Neighbor Bootstrap For Resampling Hydrologic Time Series
inferredcites →https://doi.org/10.1038/srep10312openalex-snapshot
Application of high-dimensional feature selection: evaluation for genomic prediction in man
inferredcites →https://doi.org/10.1057/palgrave.jors.2601545openalex-snapshot
Benchmarking state-of-the-art classification algorithms for credit scoring
inferredcites →https://doi.org/10.1057/palgrave.jors.2601976openalex-snapshot
Data mining feature selection for credit scoring models
inferredcites →https://doi.org/10.1080/14786440009463897openalex-snapshot
X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling
inferredcites →https://doi.org/10.1086/209665openalex-snapshot
Forecasting Bankruptcy More Accurately: A Simple Hazard Model
inferredcites →https://doi.org/10.1093/rof/8.4.537openalex-snapshot
Bankruptcy Prediction with Industry Effects
inferredcites →https://doi.org/10.1109/21.97458openalex-snapshot
A survey of decision tree classifier methodology
inferredcites →https://doi.org/10.1109/csae.2011.5952826openalex-snapshot
Applying decision tree to predict bankruptcy
inferredcites →https://doi.org/10.1109/tcyb.2015.2403356openalex-snapshot
Block-Row Sparse Multiview Multilabel Learning for Image Classification
inferredcites →https://doi.org/10.1109/tit.1968.1054102openalex-snapshot
On the mean accuracy of statistical pattern recognizers
inferredcites →https://doi.org/10.1109/tkde.2010.99openalex-snapshot
Missing Value Estimation for Mixed-Attribute Data Sets
inferredcites →https://doi.org/10.1111/coin.12200openalex-snapshot
A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification
inferredcites →https://doi.org/10.1111/j.1467-985x.1997.00078.xopenalex-snapshot
Statistical Classification Methods in Consumer Credit Scoring: A Review
inferredcites →https://doi.org/10.1111/j.1540-6261.1968.tb00843.xopenalex-snapshot
FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY
inferredcites →https://doi.org/10.1111/j.1540-6261.1985.tb04949.xopenalex-snapshot
Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress
inferredcites →https://doi.org/10.1111/j.1540-6261.1987.tb04576.xopenalex-snapshot
Credit Granting: A Comparative Analysis of Classification Procedures
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1162/evco_a_00077openalex-snapshot
No Free Lunch and Benchmarks
inferredcites →https://doi.org/10.1186/s40537-018-0143-6openalex-snapshot
Step away from stepwise
inferredcites →https://doi.org/10.1214/009053604000000067openalex-snapshot
Least angle regression
inferredcites →https://doi.org/10.2307/2982566openalex-snapshot
Regression Analysis by Example.
inferredcites →https://doi.org/10.3233/ida-1997-1102openalex-snapshot
Data Preprocessing and Intelligent Data Analysis
inferredcites →https://doi.org/10.3390/su11030699openalex-snapshot
An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments
inferredcites →https://doi.org/10.5282/edoc.17143openalex-snapshot
Recent methods from statistics and machine learning for credit scoring

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4730445openalex-extracted
How can Consumers Without Credit History Benefit from the Use of Information Processing and Machine Learning Tools by Financial Institutions?
inferred← citeshttps://doi.org/10.1002/cpe.7637openalex-snapshot
Intelligent credit scoring using deep learning methods
inferred← citeshttps://doi.org/10.1002/for.3284openalex-snapshot
Default Prediction Framework With Optimal Feature Set and Matching Ratio
inferred← citeshttps://doi.org/10.1002/jcla.70015openalex-snapshot
Investigation of Occupational Health and Safety Levels in Genetic Disease Centers in Istanbul
inferred← citeshttps://doi.org/10.1007/978-3-031-08277-1_14openalex-snapshot
Feature Selection for Credit Risk Classification
inferred← citeshttps://doi.org/10.1007/978-3-031-66958-3_32openalex-snapshot
Prediction of Incident Atrial Fibrillation in Population with Ischemic Heart Disease Using Machine Learning with Radiomics and ECG Markers
inferred← citeshttps://doi.org/10.1007/978-3-031-69237-6_15openalex-snapshot
Integrated Performance Measurement System: Prevention of Bankruptcy
inferred← citeshttps://doi.org/10.1007/978-3-032-01133-6_8openalex-snapshot
Upgrading Credit Scoring Assessment in Banking Using Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/s00521-025-11520-yopenalex-snapshot
Best practices for responsible machine learning in credit scoring
inferred← citeshttps://doi.org/10.1007/s10462-025-11416-2openalex-snapshot
Machine learning powered financial credit scoring: a systematic literature review
inferred← citeshttps://doi.org/10.1007/s10614-025-11169-8openalex-snapshot
FEIS: A Credit Risk Assessment Model Combining Feature Engineering Approach and Interpretable Submodels
inferred← citeshttps://doi.org/10.1007/s41060-022-00373-6openalex-snapshot
Stability of filter feature selection methods in data pipelines: a simulation study
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.119201openalex-snapshot
Profit scoring for credit unions using the multilayer perceptron, XGBoost and TabNet algorithms: Evidence from Peru
inferred← citeshttps://doi.org/10.1016/j.finr.2026.100125openalex-snapshot
A review of credit risk assessment in banking — From deterministic models to probabilistic AI
inferred← citeshttps://doi.org/10.1016/j.ipm.2024.103972openalex-snapshot
How can consumers without credit history benefit from the use of information processing and machine learning tools by financial institutions?
inferred← citeshttps://doi.org/10.1016/j.jeconbus.2023.106126openalex-snapshot
Explainable FinTech lending
inferred← citeshttps://doi.org/10.1016/j.jnnfm.2025.105438openalex-snapshot
An insight into parameter identifiability issues in the Carreau–Yasuda model: A more consistent rheological formulation for shear-thinning non-Newtonian inelastic fluids
inferred← citeshttps://doi.org/10.1016/j.lwt.2023.115708openalex-snapshot
Predicting the multispecies solid-state vinegar fermentation process using single-cell Raman spectroscopy combined with machine learning
inferred← citeshttps://doi.org/10.1016/j.tsc.2025.102063openalex-snapshot
Pre-evaluation of learning status based on logit models for music education in young children with special needs
inferred← citeshttps://doi.org/10.1021/acs.jpcc.5c00872openalex-snapshot
Machine Learning Assisted Materials Classification to Boost Catalyst Design for Electrochemical Oxidation
inferred← citeshttps://doi.org/10.1038/s41598-022-25089-2openalex-snapshot
ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides
inferred← citeshttps://doi.org/10.1071/wr22155openalex-snapshot
Automated Tasmanian devil segmentation and devil facial tumour disease classification
inferred← citeshttps://doi.org/10.1080/10826084.2025.2516738openalex-snapshot
Identifying Risk Factors of Substance Use in Finland: A Machine Learning Approach
inferred← citeshttps://doi.org/10.1108/jefas-09-2021-0193openalex-snapshot
A recent review on optimisation methods applied to credit scoring models
inferred← citeshttps://doi.org/10.1109/ccem60455.2023.00019openalex-snapshot
Correlate and Classify the Purpose of Investment Using Chi-Square Measure and SVM
inferred← citeshttps://doi.org/10.1109/iccteie60099.2023.10366576openalex-snapshot
A Comparative Analysis of Machine Learning Algorithms for Credit Risk Scoring using Chi-Square Feature Selection
inferred← citeshttps://doi.org/10.1109/ice3is59323.2023.10335432openalex-snapshot
Improving Creditworthiness Prediction Using Preprocessing Stages and Feature Selection
inferred← citeshttps://doi.org/10.1109/icoait67446.2025.11308825openalex-snapshot
Predicting Loan Approval Decisions Using Logistic Regression with SMOTE for Imbalanced Data
inferred← citeshttps://doi.org/10.1109/infoteh60418.2024.10496021openalex-snapshot
Enhancing Credit Scoring Models: Unveiling the Impact of Data Preprocessing and Feature Selection Techniques: Case Study based on Microfinancial institution in Bosnia and Herzegovina
inferred← citeshttps://doi.org/10.1109/smart59791.2023.10428399openalex-snapshot
A Logistic Regression Based Credit Risk Assessment Using WoE Bining and Enhanced Feature Engineering Approach ANOVA and Chi-Square
inferred← citeshttps://doi.org/10.1142/s0219622025500427openalex-snapshot
A Feature Selection Approach Based on Information Theory with Application to the International Monetary Fund and World Bank Economic Datasets
inferred← citeshttps://doi.org/10.1142/s2424786325500124openalex-snapshot
A novel hybrid model combining dimensionality reduction techniques and classification algorithms for credit scoring
inferred← citeshttps://doi.org/10.1177/18758967251369775openalex-snapshot
Improving Credit Scoring with Feature Selection and Predictive Modeling
inferred← citeshttps://doi.org/10.1186/s40854-024-00689-1openalex-snapshot
A dimension reduction assisted credit scoring method for big data with categorical features
inferred← citeshttps://doi.org/10.14445/23488549/ijece-v10i11p105openalex-snapshot
Enhancing Credit Score Analysis: A Novel Approach with Random Forest and Kernel SVM
inferred← citeshttps://doi.org/10.14453/aabfj.v16i5.04openalex-snapshot
Predicting Credit Ratings using Deep Learning Models – An Analysis of the Indian IT Industry
inferred← citeshttps://doi.org/10.17762/ijritcc.v11i8s.7227openalex-snapshot
A Novel Chaos Quasi-Oppositional based Flamingo Search Algorithm with Simulated Annealing for Feature Selection
inferred← citeshttps://doi.org/10.2139/ssrn.5148767openalex-snapshot
Credit Scoring Using Self-Adaptive Memetic Algorithm with Clustering-Adjusted Cutoff Point
inferred← citeshttps://doi.org/10.3390/electronics11244086openalex-snapshot
XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques
inferred← citeshttps://doi.org/10.3390/geomatics1040023openalex-snapshot
Entropy-Based Hybrid Integration of Random Forest and Support Vector Machine for Landslide Susceptibility Analysis
inferred← citeshttps://doi.org/10.3390/math10244800openalex-snapshot
The Possibilities of Using Scoring to Determine the Relevance of Software Development Tenders
inferred← citeshttps://doi.org/10.3390/math12060831openalex-snapshot
Forward Selection of Relevant Factors by Means of MDR-EFE Method
inferred← citeshttps://doi.org/10.3390/risks13020020openalex-snapshot
Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances
inferred← citeshttps://doi.org/10.3390/su16177532openalex-snapshot
The Exploration of Predictors for Peruvian Teachers’ Life Satisfaction through an Ensemble of Feature Selection Methods and Machine Learning
inferred← citeshttps://doi.org/10.3390/sym14081654openalex-snapshot
Automatic Assessment of Abdominal Exercises for the Treatment of Diastasis Recti Abdominis Using Electromyography and Machine Learning
inferred← citeshttps://doi.org/10.34133/icomputing.0365openalex-snapshot
Large Language Model-Enhanced Mutual Node-Edge Reinforcement for Inductive Fraud Detection in Financial Systems
inferred← citeshttps://doi.org/10.37394/23207.2024.21.192openalex-snapshot
A Feature Elimination Machine Learning Model for Credit Assessment and Repayment Behavior Prediction in Marketplace Lending
inferred← citeshttps://doi.org/10.48084/etasr.12649openalex-snapshot
A Robust Approach to Credit Scoring with Deep Learning and Embedded Methods
inferred← citeshttps://doi.org/10.48084/etasr.15590openalex-snapshot
Metaheuristic-Driven Feature Selection for Machine Learning-Based Credit Scoring
inferred← citeshttps://doi.org/10.5753/sbbd.2024.243462openalex-snapshot
Avaliação de Técnicas de Balanceamento de Dados na Detecção de Fraude em Transações Online de Cartão de Crédito
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.