A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C

Ramon, Yanou; Martens, David; Provost, Foster; Evgeniou, Theodoros

doi:10.1007/s11634-020-00418-3

Cited by 62 publications

(56 citation statements)

References 33 publications

(66 reference statements)

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“…In medical applications, the explainability of predictions is paramount [ 49 ], which is the reason for the trending research topic of explainable artificial intelligence (XAI). Two of the most popular methods for explaining the predictions of traditional black-box models are SHAP (Shapley Additive exPlanation) and LIME (Local Interpretable Model-agnostic Explanations), which also have open-sourced libraries that are integrated into other machine learning toolkits [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

Dimitrievski

Zdravevski

Lameski

et al. 2021

Sensors

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Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients’ sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.

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Section: Methodsmentioning

confidence: 99%

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

Dimitrievski

Zdravevski

Lameski

et al. 2021

Sensors

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“…2014; Ramon et al 2020), or network data (Óskarsdóttir et al 2020), which are not captured appropriately by standard implementations of these models. This calls for new mathematical optimization formulations and/or numerical solution approaches to address these complexities adequately.…”

Section: Challenges For the Futurementioning

confidence: 99%

Mathematical optimization in classification and regression trees

Carrizosa

Molero-Río

Morales

2021

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Classification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as cost-sensitivity, explainability, and fairness, and to deal with complex data, such as functional data.

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“…Counterfactuals provide intuitive and human-friendly explanations. Therefore, a lot of corresponding methods have been proposed [36]- [39], including counterfactual modifications of LIME [40], [41].…”

Section: Related Workmentioning

confidence: 99%

Uncertainty Interpretation of the Machine Learning Survival Model Predictions

Utkin¹,

Zaborovsky

Kovalev

et al. 2021

IEEE Access

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A method for interpreting uncertainty of predictions provided by machine learning survival models is proposed. It is called UncSurvEx and aims to determine which features of an analyzed example lead to uncertain predictions of an explainable black-box survival model. One of the ideas behind the proposed method is to approximate the uncertainty measure of a local black-box survival model prediction by the uncertainty measure of the Cox proportional hazards model at the local area around a test example. The linear relationship between covariates and predictions in the corresponding Cox model allows determining quantitative impacts of covariates on the uncertainty measure. A specific certainty measure of the survival function, taking into account the most uncertain survival function, is introduced to interpret the prediction uncertainty. The L 2 -norm is used to compute the distance between survival functions. The method leads to an unconstrained non-convex optimization problem which is solved by means of the well-known Broyden-Fletcher-Goldfarb-Shanno algorithm. A lot of numerical experiments demonstrate the uncertainty interpretation method.INDEX TERMS explainable AI, survival analysis, uncertainty, censored data, the Cox model I. INTRODUCTION A. EXPLANATION OF SURVIVAL MODELS AND UNCERTAINTY INTERPRETATION

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A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C

Cited by 62 publications

References 33 publications

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

Mathematical optimization in classification and regression trees

Uncertainty Interpretation of the Machine Learning Survival Model Predictions

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