Case-based reasoning ensemble and business application: A computational approach from multiple case representations driven by randomness

Li, Hui; Sun, Jinwei

doi:10.1016/j.eswa.2011.09.017

Cited by 19 publications

(5 citation statements)

References 49 publications

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“…Furthermore, many studies are dedicated to the use of various techniques of combining the results of base model classification: such as neural networks in the form of self-organizing maps (SOMs), rough sets techniques, case-based reasoning and classifier consensus methods. Examples of the use of this type of ensemble classifiers were examined by Ala'raj and Abbod (2016), Du Jardin (2018), Chuang (2013) and Li and Sun (2012).…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

Pisula

2020

JRFM

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This publication presents the methodological aspects of designing of a scoring model for an early prediction of bankruptcy by using ensemble classifiers. The main goal of the research was to develop a scoring model (with good classification properties) that can be applied in practice to assess the risk of bankruptcy of enterprises in various sectors. For the data sample, which included 1739 Polish businesses (of which 865 were bankrupt and 875 had no risk of bankruptcy), a genetic algorithm was applied to select the optimum set of 19 bankruptcy indicators, on the basis of which the classification accuracy of a number of ensemble classifier model variants (boosting, bagging and stacking) was estimated and verified. The classification effectiveness of ensemble models was compared with eight classical individual models which made use of single classifiers. A GBM-based ensemble classifier model offering superior classification capabilities was used in practice to design a scoring model, which was applied in comparative evaluation and bankruptcy risk analysis for businesses from various sectors and of different sizes from the Podkarpackie Voivodeship in 2018 (over a time horizon of up to two years). The approach applied can also be used to assess credit risk for corporate borrowers.

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Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

Pisula

2020

JRFM

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“…In order to solve the problem of trust in the supply chain partners, Li H, Sun J, et al (2012) [3] proposed the integrated supply chain trust diagnosis method based on inductive reasoning method, and compared with multivariate discriminant analysis, logistic regression, single euclidean case-based reasoning, and single inductive case-based reasoning, the experimental results show that this method has obvious advantages, which greatly promotes the improvement and application of case based reasoning technology. Li H, Sun J (2012) [4] proposed a method to improve the case based reasoning method to improve the classification performance of case based reasoning technology, and compared with discriminant analysis multivariate, regression logistic, the classical CBR algorithm and, it was found that the integrated case based reasoning method greatly improved the performance of prediction. In order to carry out effective classification of small sample data, Xu Y H, Li H, et al (2014) [5] proposed a new NT-SMOTE data mining technology based on the spatial triangle and over sampling technology, which effectively solves the problem of large data distribution of small sample.…”

Section: Literature Reviewmentioning

confidence: 99%

Internet Big Data Information Analysis and Power Intelligent Automation Risk Prediction Based on Case Based Reasoning

Xu¹,

Tian²

2015

Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications

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The development of the Internet makes it easier to get data. However, it is more urgent to obtain effective information on the large data which is redundant. Based on the risk prediction system of power intelligent automation, this paper tries to solve the problems of enterprise risk management, and tries to make human beings free from the heavy human and mental labor. In order to solve the problem of large data analysis, this paper constructs a close triangle case based reasoning model based on the data mining technology of NT-SMOTE and the case based reasoning technology. Compare with NT-MDA, NT-Logit, NT-Probit, NT-DT, NT-SVM and other methods, the experimental results show that NT-CBR is more effective than other methods of intelligent risk prediction.

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“…Arshadi and Jurisica [13] present an ensemble method for combining predictions of a set of classifiers built based on disjoint subsets of cases from the original case base, for which the case features are selected locally by using logistic regression. Li and Sun [14] propose using an ensemble of CBR systems, with randomly generated feature subsets used for similarity assessment in each individual CBR system, and forming the final solution by combining the results of those individual systems. However, to our knowledge, previous CBR research has not considered the use of ensembles of case adaptation rules.…”

Section: Ensemble Methods In Cbrmentioning

confidence: 99%

Extending Case Adaptation with Automatically-Generated Ensembles of Adaptation Rules

Jalali

Leake

2013

Case-Based Reasoning Research and Development

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Abstract. Case-based regression often relies on simple case adaptation methods. This paper investigates new approaches to enriching the adaptation capabilities of case-based regression systems, based on the use of ensembles of adaptation rules generated from the case base. The paper explores both local and global methods for generating adaptation rules from the case base, and presents methods for ranking the generated rules and combining the resulting ensemble of adaptation rules to generate new solutions. It tests these methods in five standard domains, evaluating their performance compared to four baseline methods, standard k-NN, linear regression, locally weighted linear regression, and an ensemble of k-NN predictors with different feature subsets. The results demonstrate that the proposed method generally outperforms the baselines and that the accuracy of adaptation based on locally-generated rules is highly competitive with that of global rule-generation methods with much greater computational cost.

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Case-based reasoning ensemble and business application: A computational approach from multiple case representations driven by randomness

Cited by 19 publications

References 49 publications

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

Internet Big Data Information Analysis and Power Intelligent Automation Risk Prediction Based on Case Based Reasoning

Extending Case Adaptation with Automatically-Generated Ensembles of Adaptation Rules

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