Jie Sun

Affiliations:
  • School of Economics and Management, Zhejiang Normal University
  • Harbin Institute of Technology


According to our database1, Jie Sun authored at least 42 papers between 2006 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2022
Multi-class imbalanced enterprise credit evaluation based on asymmetric bagging combined with light gradient boosting machine.
Appl. Soft Comput., 2022

2021
Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods.
Inf. Sci., 2021

2020
Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting.
Inf. Fusion, 2020

2018
Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates.
Inf. Sci., 2018

2017
Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble.
Knowl. Based Syst., 2017

2016
The dynamic financial distress prediction method of EBW-VSTW-SVM.
Enterp. Inf. Syst., 2016

2015
Dynamic credit scoring using B & B with incremental-SVM-ensemble.
Kybernetes, 2015

2014
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches.
Knowl. Based Syst., 2014

Imbalance-oriented SVM methods for financial distress prediction: a comparative study among the new SB-SVM-ensemble method and traditional methods.
J. Oper. Res. Soc., 2014

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process.
Int. J. Syst. Sci., 2014

Statistics-based wrapper for feature selection: An implementation on financial distress identification with support vector machine.
Appl. Soft Comput., 2014

2013
Concept Drift-Oriented Adaptive and Dynamic Support Vector Machine Ensemble With Time Window in Corporate Financial Risk Prediction.
IEEE Trans. Syst. Man Cybern. Syst., 2013

Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms.
Int. J. Syst. Sci., 2013

Forecasting business failure using two-stage ensemble of multivariate discriminant analysis and logistic regression.
Expert Syst. J. Knowl. Eng., 2013

2012
Case-based reasoning ensemble and business application: A computational approach from multiple case representations driven by randomness.
Expert Syst. Appl., 2012

Financial distress prediction using support vector machines: Ensemble vs. individual.
Appl. Soft Comput., 2012

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis.
Appl. Soft Comput., 2012

2011
SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams.
Knowl. Based Syst., 2011

The random subspace binary logit (RSBL) model for bankruptcy prediction.
Knowl. Based Syst., 2011

Principal component case-based reasoning ensemble for business failure prediction.
Inf. Manag., 2011

Dynamic financial distress prediction using instance selection for the disposal of concept drift.
Expert Syst. Appl., 2011

AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies.
Expert Syst. Appl., 2011

Predicting business failure using support vector machines with straightforward wrapper: A re-sampling study.
Expert Syst. Appl., 2011

Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction.
Expert Syst. Appl., 2011

Predicting business failure using forward ranking-order case-based reasoning.
Expert Syst. Appl., 2011

Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction.
Comput. Oper. Res., 2011

On performance of case-based reasoning in Chinese business failure prediction from sensitivity, specificity, positive and negative values.
Appl. Soft Comput., 2011

2010
Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods.
Expert Syst. Appl., 2010

On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction.
Expert Syst. Appl., 2010

Business failure prediction using hybrid<sup>2</sup> case-based reasoning (H<sup>2</sup>CBR).
Comput. Oper. Res., 2010

2009
Gaussian case-based reasoning for business failure prediction with empirical data in China.
Inf. Sci., 2009

Financial distress prediction based on serial combination of multiple classifiers.
Expert Syst. Appl., 2009

Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors.
Expert Syst. Appl., 2009

Majority voting combination of multiple case-based reasoning for financial distress prediction.
Expert Syst. Appl., 2009

Predicting business failure using multiple case-based reasoning combined with support vector machine.
Expert Syst. Appl., 2009

Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II.
Eur. J. Oper. Res., 2009

Financial distress early warning based on group decision making.
Comput. Oper. Res., 2009

2008
Data mining method for listed companies' financial distress prediction.
Knowl. Based Syst., 2008

Ranking-order case-based reasoning for financial distress prediction.
Knowl. Based Syst., 2008

Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers.
Expert Syst. Appl., 2008

2006
An Application of Support Vector Machine to Companies' Financial Distress Prediction.
Proceedings of the Modeling Decisions for Artificial Intelligence, 2006

Financial Distress Prediction Based on Similarity Weighted Voting CBR.
Proceedings of the Advanced Data Mining and Applications, Second International Conference, 2006


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