A Partially Interpretable Adaptive Softmax Regression for Credit Scoring

Munkhdalai, Lkhagvadorj; Ryu, Keun Ho; Namsrai, Oyun-Erdene; Theera-Umpon, Nipon

doi:10.3390/app11073227

Cited by 11 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the Batch processing is performed with each convolutional layer of the assessment model. Each hidden layer's feature distribution constantly updates in response to parameter changes, and the distribution progressively updates the activation functions [54][55][56].…”

Section: Case Studymentioning

confidence: 99%

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

Srinivasu¹,

Sandhya²,

Jhaveri

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

Introduction. Artificial intelligence (AI) models have been employed to automate decision-making, from commerce to more critical fields directly affecting human lives, including healthcare. Although the vast majority of these proposed AI systems are considered black box models that lack explainability, there is an increasing trend of attempting to create medical explainable Artificial Intelligence (XAI) systems using approaches such as attention mechanisms and surrogate models. An AI system is said to be explainable if humans can tell how the system reached its decision. Various XAI-driven healthcare approaches and their performances in the current study are discussed. The toolkits used in local and global post hoc explainability and the multiple techniques for explainability pertaining the Rational, Data, and Performance explainability are discussed in the current study. Methods. The explainability of the artificial intelligence model in the healthcare domain is implemented through the Local Interpretable Model-Agnostic Explanations and Shapley Additive Explanations for better comprehensibility of the internal working mechanism of the original AI models and the correlation among the feature set that influences decision of the model. Results. The current state-of-the-art XAI-based and future technologies through XAI are reported on research findings in various implementation aspects, including research challenges and limitations of existing models. The role of XAI in the healthcare domain ranging from the earlier prediction of future illness to the disease’s smart diagnosis is discussed. The metrics considered in evaluating the model’s explainability are presented, along with various explainability tools. Three case studies about the role of XAI in the healthcare domain with their performances are incorporated for better comprehensibility. Conclusion. The future perspective of XAI in healthcare will assist in obtaining research insight in the healthcare domain.

show abstract

Section: Case Studymentioning

confidence: 99%

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

Srinivasu¹,

Sandhya²,

Jhaveri

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Moreover, credit-scoring methods [10][11][12][13], which assign the credit level for applicants, are also related to our problem. The traditional machine learning methods such as decision tree [13] and softmax regression [11] methods are employed to build a classifier to generate the credit levels. However, these methods ignore the semantic information of statements, which cannot be used for our problem.…”

Section: Loan Recommendationmentioning

confidence: 99%

“…However, most of these works are designed for instant feedback applications and cannot be used for loan recommendations. Apart from these methods, previous credit scoring methods [10][11][12][13] are also related to our problem but these methods cannot handle cold start problems. Moreover, for the third challenge, there is no existing work considering the mixture evaluation metrics.…”

Section: Introductionmentioning

confidence: 99%

CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization

Zhuang¹,

Wu²,

Liu³

2022

Applied Sciences

View full text Add to dashboard Cite

Recommending loan products to applicants would benefit many financial businesses and individuals. Nevertheless, many loan products suffer from the cold start problem; i.e., there are no available historical data for training the recommendation model. Considering the delayed feedback and the complex semantic properties of loans, methods for general cold start recommendation cannot be directly used. Moreover, existing loan recommendation methods ignore the default risk, which should be evaluated along with the approval rate. To solve these challenges, we propose CSRLoan for cold start loan recommendation. CSRLoan employs pretraining techniques to learn the embeddings of statements, which captures the intrinsic semantic information of different loans. For recommendation, we design a dual neural matrix factorization(NMF) model, which can not only capture the semantic information of both loan products and applicants but also generate the recommendation results and default risk at the same time. Extensive experiments have been conducted on real-world datasets to evaluate the effectiveness and efficiency of the proposed CSRLoan.

show abstract

“…We have recognized this trend as more crucial for health care experts to overcome several challenges such as readiness of outcomes. Meanwhile, few numbers of studies intended to solve the black-box issue in the health care area [16,[30][31][32][33][34].…”

Section: B Explainable Artificial Intelligence In Health Care Applicationsmentioning

confidence: 99%

“…The local approach can explain the conditional interaction between features and classes for a single instance. Local explanations can be more accurate than global explanations [34]. Thus, we propose DeepSHAP based deep learning framework, which incorporated a feature selection technique for prediction and interpretation of NCDs in order to solve existing both problems.…”

Section: B Explainable Artificial Intelligence In Health Care Applicationsmentioning

confidence: 99%

Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction

et al. 2021

Self Cite

View full text Add to dashboard Cite

The rapid rise of non-communicable diseases (NCDs) becomes one of the serious health issues and the leading cause of death worldwide. In recent years, artificial intelligence-based systems have been developed to assist clinicians in decision-making to reduce morbidity and mortality. However, a common drawback of these modern studies is related to explanations of their output. In other words, understanding the inner logic behind the predictions is hidden to the end-user. Thus, clinicians struggle to interpret these models because of their black-box nature, and hence they are not acceptable in the medical practice. To address this problem, we have proposed a Deep Shapley Additive Explanations (DeepSHAP) based deep neural network framework equipped with a feature selection technique for NCDs prediction and explanation among the population in the United States. Our proposed framework comprises three components: First, representative features are done based on the elastic net-based embedded feature selection technique; second a deep neural network classifier is tuned with the hyper-parameters and used to train the model with the selected feature subset; third, two kinds of model explanation are provided by the DeepSHAP approach. Herein, (I) explaining the risk factors that affected the model's prediction from the population-based perspective; (II) aiming to explain a single instance from the human-centered perspective. The experimental results indicated that the proposed model outperforms various state-of-the-art models. In addition, the proposed model can improve the medical understanding of NCDs diagnosis by providing general insights into the changes in disease risk at the global and local levels. Consequently, DeepSHAP based explainable deep learning framework contributes not only to the medical decision support systems but also can provide to real-world needs in other domains.

show abstract

A Partially Interpretable Adaptive Softmax Regression for Credit Scoring

Cited by 11 publications

References 39 publications

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization

Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction

Contact Info

Product

Resources

About