Information asset analysis: credit scoring and credit suggestion

Silva, Fábio; Analide, César

doi:10.1504/ijeb.2011.042542

Cited by 3 publications

(2 citation statements)

References 13 publications

(14 reference statements)

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“…This is a generic idea which may have different implementations depending on the algorithm being studied. In the literature this approach can be found in linear classification algorithms [11], where a linear Machine Learning algorithm is exploited to find how changes in coefficients or inputs change the final decision, as well as in black box models such as mutlilayer perceptrons [12]. Indeed, explanations are often more valuable when it comes to black box models, as there is here, clearly, a trade-off between interpretability and accuracy [13]: models that are generally more accurate, such as Deep Learning, are usually also harder to explain.…”

Section: Explainability In Machine Learningmentioning

confidence: 99%

A predictive and user-centric approach to Machine Learning in data streaming scenarios

et al. 2022

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Section: Explainability In Machine Learningmentioning

confidence: 99%

A predictive and user-centric approach to Machine Learning in data streaming scenarios

et al. 2022

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“…Black box models, such as mutlilayer perceptrons, can also embed this approach. In [9], a genetic algorithm is used to search an output domain to provide suggestions for credit risk assessment, which can be perceived as an approach to interpret and explain a neural network decision process. This approach is similar to a technique known as LIME: Local Interpretable Model-Agnostic Explanations [10], which develops an approximation of the model by testing what happens when certain aspects within the input of the model are changed.…”

Section: Approaches To Enhance Explainability and Interpretabilitymentioning

confidence: 99%

Explainable Intelligent Environments

Carneiro

Silva

Guimarães

et al. 2020

Advances in Intelligent Systems and Computing

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The main focus of an Intelligent environment, as with other applications of Artificial Intelligence, is generally on the provision of good decisions towards the management of the environment or the support of human decision-making processes. The quality of the system is often measured in terms of accuracy or other performance metrics, calculated on labeled data. Other equally important aspects are usually disregarded, such as the ability to produce an intelligible explanation for the user of the environment. That is, asides from proposing an action, prediction, or decision, the system should also propose an explanation that would allow the user to understand the rationale behind the output. This is becoming increasingly important in a time in which algorithms gain increasing importance in our lives and start to take decisions that significantly impact them. So much so that the EU recently regulated on the issue of a "right to explanation". In this paper we propose a Humancentric intelligent environment that takes into consideration the domain of the problem and the mental model of the Human expert, to provide intelligible explanations that can improve the efficiency and quality of the decision-making processes.

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A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers

Fallah

Alborzi

Mohammadzadeh

2017

Eng. Technol. Appl. Sci. Res.

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Financial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. Moreover, this paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. Hence, it helps us to identify which hybrid model can achieve the best performance for credit scoring purposes. Using four real-life credit datasets, the experimental results show that the model of (KNN-NN-SVMPSO)-(DL)-(DBSCAN) delivers the highest prediction accuracy and the lowest error rates.

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Information asset analysis: credit scoring and credit suggestion

Cited by 3 publications

References 13 publications

A predictive and user-centric approach to Machine Learning in data streaming scenarios

A predictive and user-centric approach to Machine Learning in data streaming scenarios

Explainable Intelligent Environments

A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers

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