Philippe du Jardin

Orcid: 0000-0002-2562-654X

According to our database1, Philippe du Jardin authored at least 16 papers between 2010 and 2026.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
A multi-graph learning framework to fuse heterogeneous market information for stock forecasting.
Expert Syst. Appl., 2026

2025
Ensemble learning for operations research and business analytics.
Ann. Oper. Res., October, 2025

MRRFGNN: Multi-relation reconstruction and fusion graph neural network for stock crash prediction.
Inf. Sci., 2025

2023
Designing topological data to forecast bankruptcy using convolutional neural networks.
Ann. Oper. Res., June, 2023

2021
Forecasting corporate failure using ensemble of self-organizing neural networks.
Eur. J. Oper. Res., 2021

Dynamic self-organizing feature map-based models applied to bankruptcy prediction.
Decis. Support Syst., 2021

Forecasting bankruptcy using biclustering and neural network-based ensembles.
Ann. Oper. Res., 2021

2018
Failure pattern-based ensembles applied to bankruptcy forecasting.
Decis. Support Syst., 2018

2017
Dynamics of firm financial evolution and bankruptcy prediction.
Expert Syst. Appl., 2017

2016
A two-stage classification technique for bankruptcy prediction.
Eur. J. Oper. Res., 2016

2015
SOM-ELM - Self-Organized Clustering using ELM.
Neurocomputing, 2015

MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model.
Neurocomputing, 2015

Bankruptcy prediction using terminal failure processes.
Eur. J. Oper. Res., 2015

2012
Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time.
Eur. J. Oper. Res., 2012

2011
Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model.
Decis. Support Syst., 2011

2010
Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy.
Neurocomputing, 2010


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