dsotofor / Manuscrit

Commit 399d77d3dc57027ebca78e9b57540afd0f49b9ca

Authored by dsotofor 2025-04-18 09:20:55 +0200

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chapters/CBR.aux
chapters/CBR.tex
main.aux
main.bbl
main.bib
main.blg
main.log
main.pdf
main.synctex.gz

...	...	@@ -26,6 +26,8 @@
26	26	\citation{10.1007/978-3-030-58342-2_5}
27	27	\citation{8495930}
28	28	\citation{Obeid}
	29	+\citation{buildings13030651}
	30	+\citation{YU2023110163}
29	31	\citation{ROLDANREYES20151}
30	32	\citation{ROLDANREYES20151}
31	33	\citation{Obeid}

...	...	@@ -52,9 +52,9 @@
52	52
53	53	--- Méthodes d'ensemble
54	54
55		-Uysal et al. [...] mettent en œuvre un RáPC avec une méthode d'agrégation bootstrap (bagging) pour améliorer la précision du CBR et réduire la variance.
	55	+\cite{buildings13030651} mettent en œuvre un RáPC avec une méthode d'agrégation bootstrap (bagging) pour améliorer la précision du CBR et réduire la variance.
56	56
57		-Un modèle d'ensemble fondé sur le raisonnement à partir de cas est proposé par Yu et al. [...] appliqué à la prédiction financière et au remplissage des données manquantes. Dans ce cas, pour retrouver les plus proches voisins, le modèle utilise trois mesures de distance différentes et une étape de vote pour l'intégration. Le modèle a été testé avec une base de données comportant onze dimensions d'informations financières provenant de 249 entreprises. La comparaison est faite avec deux objectifs. Premièrement, le remplissage des données manquantes avec d'autres algorithmes tels que KNN ou RandomForest, et deuxièmement, la comparaison de la prédiction avec des algorithmes uniques utilisant une métrique de distance spécifique. En effet, les résultats montrent une meilleure performance dans le remplissage des données manquantes et les meilleurs résultats dans la prédiction.
	57	+Un modèle d'ensemble fondé sur le raisonnement à partir de cas est proposé par \cite{YU2023110163} appliqué à la prédiction financière et au remplissage des données manquantes. Dans ce cas, pour retrouver les plus proches voisins, le modèle utilise trois mesures de distance différentes et une étape de vote pour l'intégration. Le modèle a été testé avec une base de données comportant onze dimensions d'informations financières provenant de 249 entreprises. La comparaison est faite avec deux objectifs. Premièrement, le remplissage des données manquantes avec d'autres algorithmes tels que KNN ou RandomForest, et deuxièmement, la comparaison de la prédiction avec des algorithmes uniques utilisant une métrique de distance spécifique. En effet, les résultats montrent une meilleure performance dans le remplissage des données manquantes et les meilleurs résultats dans la prédiction.
58	58
59	59	----------------------------------
60	60

...	...	@@ -85,9 +85,11 @@
85	85	\bibcite{10.1007/978-3-031-63646-2_13}{Soto-Forero et~al., 2024b}
86	86	\bibcite{SU2022109547}{Su et~al., 2022}
87	87	\bibcite{8495930}{Supic, 2018}
	88	+\bibcite{buildings13030651}{Uysal and Sonmez, 2023}
88	89	\bibcite{WANG2021331}{Wang et~al., 2021}
89	90	\bibcite{wolf2024keep}{Wolf et~al., 2024}
90	91	\bibcite{9627973}{Xu et~al., 2021}
	92	+\bibcite{YU2023110163}{Yu and Li, 2023}
91	93	\bibcite{ZHANG2021100025}{Zhang. and Aslan, 2021}
92	94	\bibcite{ZHAO2023118535}{Zhao et~al., 2023}
93	95	\bibcite{Zhou2021}{Zhou and Wang, 2021}

...	...	@@ -303,6 +303,12 @@
303	303	Technologies: Infrastructure for Collaborative Enterprises (WETICE)}, pages
304	304	175--178.
305	305
	306	+\bibitem[Uysal and Sonmez, 2023]{buildings13030651}
	307	+Uysal, F. and Sonmez, R. (2023).
	308	+\newblock Bootstrap aggregated case-based reasoning method for conceptual cost
	309	+ estimation.
	310	+\newblock {\em Buildings}, 13(3).
	311	+
306	312	\bibitem[Wang et~al., 2021]{WANG2021331}
307	313	Wang, F., Liao, F., Li, Y., and Wang, H. (2021).
308	314	\newblock A new prediction strategy for dynamic multi-objective optimization
...	...	@@ -321,6 +327,12 @@
321	327	Xu, S., Cai, W., Xia, H., Liu, B., and Xu, J. (2021).
322	328	\newblock Dynamic metric accelerated method for fuzzy clustering.
323	329	\newblock {\em IEEE Access}, 9:166838--166854.
	330	+
	331	+\bibitem[Yu and Li, 2023]{YU2023110163}
	332	+Yu, L. and Li, M. (2023).
	333	+\newblock A case-based reasoning driven ensemble learning paradigm for
	334	+ financial distress prediction with missing data.
	335	+\newblock {\em Applied Soft Computing}, 137:110163.
324	336
325	337	\bibitem[Zhang. and Aslan, 2021]{ZHANG2021100025}
326	338	Zhang., K. and Aslan, A.~B. (2021).

...	...	@@ -1688,4 +1688,32 @@
1688	1688	keywords = {Case-based reasoning, Explainable artificial intelligence, Explanation methods, Explanation of image classification},
1689	1689	abstract = {Deep learning is especially remarkable in terms of image classification. However, the outcomes of models are not explainable to users due to their complex nature, having an impact on the users’ trust in the provided classifications. To solve this problem, several explanation techniques have been proposed, but they greatly depend on the nature of the images being classified and the users’ perception of the explanations. In this work, we present Case-Based Reasoning as a learning-based solution to the problem of selecting the best explanation method for the image classifications obtained by models. We propose the elicitation of a case base that reflects the human perception of the quality of the explanations and how to reuse this knowledge to select the best explanation approach for a given image classification.}
1690	1690	}
	1691	+
	1692	+@Article{buildings13030651,
	1693	+AUTHOR = {Uysal, Furkan and Sonmez, Rifat},
	1694	+TITLE = {Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation},
	1695	+JOURNAL = {Buildings},
	1696	+VOLUME = {13},
	1697	+YEAR = {2023},
	1698	+NUMBER = {3},
	1699	+ARTICLE-NUMBER = {651},
	1700	+URL = {https://www.mdpi.com/2075-5309/13/3/651},
	1701	+ISSN = {2075-5309},
	1702	+ABSTRACT = {Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages of construction projects. For this reason, numerous machine learning methods proposed in the literature that use different learning mechanisms. In recent years, the case-based reasoning (CBR) method has received particular attention in the literature for conceptual cost estimation of construction projects that use similarity-based learning principles. Despite the fact that CBR provides a powerful and practical alternative for conceptual cost estimation, one of the main criticisms about CBR is its low prediction performance when there is not a sufficient number of cases. This paper presents a bootstrap aggregated CBR method for achieving advancement in CBR research, particularly for conceptual cost estimation of construction projects when a limited number of training cases are available. The proposed learning method is designed so that CBR can learn from a diverse set of training data even when there are not a sufficient number of cases. The performance of the proposed bootstrap aggregated CBR method is evaluated using three data sets. The results revealed that the prediction performance of the new bootstrap aggregated CBR method is better than the prediction performance of the existing CBR method. Since the majority of conceptual cost estimates are made with a limited number of cases, the proposed method provides a contribution to CBR research and practice by improving the existing methods for conceptual cost estimating.},
	1703	+DOI = {10.3390/buildings13030651}
	1704	+}
	1705	+
	1706	+@article{YU2023110163,
	1707	+title = {A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data},
	1708	+journal = {Applied Soft Computing},
	1709	+volume = {137},
	1710	+pages = {110163},
	1711	+year = {2023},
	1712	+issn = {1568-4946},
	1713	+doi = {https://doi.org/10.1016/j.asoc.2023.110163},
	1714	+url = {https://www.sciencedirect.com/science/article/pii/S1568494623001813},
	1715	+author = {Lean Yu and Mengxin Li},
	1716	+keywords = {Case-based reasoning, Missing data imputation, Financial distress prediction, Imputation method, Ensemble learning},
	1717	+abstract = {Financial distress prediction is often accompanied by missing sample data. For this purpose, a novel case-based reasoning (CBR) driven ensemble learning paradigm is proposed for financial distress prediction with missing data. In the proposed paradigm, three main stages, CBR-driven missing data imputation, CBR-driven single classifiers prediction, and CBR-driven ensemble result output, are involved. In the first stage, the CBR-driven missing data imputation method is used to fill in missing values in the initial dataset. Second, three different CBR-driven single classification models are constructed using Manhattan distance, Euclidean distance, and cosine distance to predict financial distress, respectively. In the final stage, the weighted majority voting strategy is used to ensemble prediction results of the CBR-driven single classification models to improve prediction accuracy and robustness. For illustration and verification, the experiments on datasets with different missing rates of six Chinese listed companies are performed. And corresponding results show that the proposed CBR-driven ensemble learning paradigm can effectively improve the imputation performance and increase the robustness of classification performance, indicating that the proposed CBR-driven ensemble learning paradigm can be used as a competitive solution to financial distress prediction with missing data.}
	1718	+}

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