Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Lahmiri, S.; Gagnon, S.
A sequential probabilistic system for bankruptcy data classification Book
IGI Global, 2018, ISBN: 978-1-5225-5644-2 1-5225-5643-5 978-1-5225-5643-5, (Publication Title: Intelligent Systems: Concepts, Methodologies, Tools, and Applications).
Abstract | Links | BibTeX | Tags: Bankruptcy prediction, Corporate finance, Data classification, Discriminant analysis, Forecasting, Human resource management, Independent variables, Neural networks, Nonlinear problems, Probabilistic systems, Real-world problem, Soft computing, Softcomputing techniques, Support vector machines
@book{lahmiri_sequential_2018,
title = {A sequential probabilistic system for bankruptcy data classification},
author = {S. Lahmiri and S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059712022&doi=10.4018%2f978-1-5225-5643-5.ch064&partnerID=40&md5=21c0d2da6087916a6c372529e2b13784},
doi = {10.4018/978-1-5225-5643-5.ch064},
isbn = {978-1-5225-5644-2 1-5225-5643-5 978-1-5225-5643-5},
year = {2018},
date = {2018-01-01},
publisher = {IGI Global},
abstract = {In the last decade, the development of bankruptcy prediction models has been one of important issues in accounting and corporate finance research fields. Indeed, bankruptcy is a critical event that yields important loss to management, shareholders, employees, and also to government. Statistical methods such as discriminant analysis, logistic and probit models were widely used for developing bankruptcy prediction systems. However, statistical-based approaches are assumes strong assumptions including linearity of the relationship among dependent and independent variables, normality of the errors which limit their applicability in bankruptcy real world problems. Recently, machine learning and soft computing techniques including artificial neural networks, support vector machines, and evolutionary intelligence have brought forth new alternatives in solving nonlinear problems with applications in bankruptcy prediction. The purpose of this chapter is to present a sequential probabilistic system for bankruptcy data classification to help manager in making decisions. © 2018, IGI Global. All rights reserved.},
note = {Publication Title: Intelligent Systems: Concepts, Methodologies, Tools, and Applications},
keywords = {Bankruptcy prediction, Corporate finance, Data classification, Discriminant analysis, Forecasting, Human resource management, Independent variables, Neural networks, Nonlinear problems, Probabilistic systems, Real-world problem, Soft computing, Softcomputing techniques, Support vector machines},
pubstate = {published},
tppubtype = {book}
}
In the last decade, the development of bankruptcy prediction models has been one of important issues in accounting and corporate finance research fields. Indeed, bankruptcy is a critical event that yields important loss to management, shareholders, employees, and also to government. Statistical methods such as discriminant analysis, logistic and probit models were widely used for developing bankruptcy prediction systems. However, statistical-based approaches are assumes strong assumptions including linearity of the relationship among dependent and independent variables, normality of the errors which limit their applicability in bankruptcy real world problems. Recently, machine learning and soft computing techniques including artificial neural networks, support vector machines, and evolutionary intelligence have brought forth new alternatives in solving nonlinear problems with applications in bankruptcy prediction. The purpose of this chapter is to present a sequential probabilistic system for bankruptcy data classification to help manager in making decisions. © 2018, IGI Global. All rights reserved.
2.
Ouyed, O.; Allili, M. S.
Feature relevance for kernel logistic regression and application to action classification Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, pp. 1325–1329, Institute of Electrical and Electronics Engineers Inc., 2014, ISBN: 978-1-4799-5208-3, (ISSN: 10514651).
Abstract | Links | BibTeX | Tags: Action classifications, Action recognition, Classification (of information), Classification methods, Classification performance, Feature relevance, Kernel logistic regression, Logistic regression, Multinomial kernels, Pattern Recognition, Supervised classification, Support vector machines
@inproceedings{ouyed_feature_2014,
title = {Feature relevance for kernel logistic regression and application to action classification},
author = {O. Ouyed and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919884283&doi=10.1109%2fICPR.2014.237&partnerID=40&md5=616038cd7924614fd633061dfed32903},
doi = {10.1109/ICPR.2014.237},
isbn = {978-1-4799-5208-3},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
pages = {1325–1329},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {An approach is proposed for incorporating feature relevance in mutinomial kernel logistic regression (MKLR) for classification. MKLR is a supervised classification method designed for separating classes with non-linear boundaries. However, it assumes all features are equally important, which may decrease classification performance when dealing with high-dimensional or noisy data. We propose a feature weighting algorithm for MKLR which automatically tunes features contribution according to their relevance for classification and reduces data over-fitting. The proposed algorithm produces more interpretable models and is more generalizable than MKLR, Kernel-SVM and LASSO methods. Application to simulated data and video action classification has provided very promising results compared to the aforementioned classification methods. © 2014 IEEE.},
note = {ISSN: 10514651},
keywords = {Action classifications, Action recognition, Classification (of information), Classification methods, Classification performance, Feature relevance, Kernel logistic regression, Logistic regression, Multinomial kernels, Pattern Recognition, Supervised classification, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
An approach is proposed for incorporating feature relevance in mutinomial kernel logistic regression (MKLR) for classification. MKLR is a supervised classification method designed for separating classes with non-linear boundaries. However, it assumes all features are equally important, which may decrease classification performance when dealing with high-dimensional or noisy data. We propose a feature weighting algorithm for MKLR which automatically tunes features contribution according to their relevance for classification and reduces data over-fitting. The proposed algorithm produces more interpretable models and is more generalizable than MKLR, Kernel-SVM and LASSO methods. Application to simulated data and video action classification has provided very promising results compared to the aforementioned classification methods. © 2014 IEEE.