Counterfactual explanations and how to find them: literature review and benchmarking
R Guidotti - Data Mining and Knowledge Discovery, 2024 - Springer
Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …
uninterpretable classifiers. One of the most valuable types of explanation consists of …
A survey of algorithmic recourse: contrastive explanations and consequential recommendations
Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Machine learning is increasingly used to inform decision-making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
A framework and benchmarking study for counterfactual generating methods on tabular data
RMB de Oliveira, D Martens - Applied Sciences, 2021 - mdpi.com
Counterfactual explanations are viewed as an effective way to explain machine learning
predictions. This interest is reflected by a relatively young literature with already dozens of …
predictions. This interest is reflected by a relatively young literature with already dozens of …
Convex density constraints for computing plausible counterfactual explanations
The increasing deployment of machine learning as well as legal regulations such as EU's
GDPR cause a need for user-friendly explanations of decisions proposed by machine …
GDPR cause a need for user-friendly explanations of decisions proposed by machine …
Model-agnostic counterfactual explanations in credit scoring
The past decade has shown a surge in the use and application of machine learning and
deep learning models across various domains. One such domain is credit scoring, where …
deep learning models across various domains. One such domain is credit scoring, where …
Efficient computation of contrastive explanations
With the increasing deployment of machine learning systems in practice, transparency and
explainability have become serious issues. Contrastive explanations are considered to be …
explainability have become serious issues. Contrastive explanations are considered to be …
Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine
H Ouchra, A Belangour… - … of Environmental & …, 2023 - journals.bilpubgroup.com
Satellite image classification is crucial in various applications such as urban planning,
environmental monitoring, and land use analysis. In this study, the authors present a …
environmental monitoring, and land use analysis. In this study, the authors present a …
Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation
O AbuAlghanam, O Adwan, MA Al Shariah… - Cybernetics and …, 2022 - sciendo.com
Learning Vector Quantization (LVQ) is one of the most widely used classification
approaches. LVQ faces a problem as when the size of data grows large it becomes slower …
approaches. LVQ faces a problem as when the size of data grows large it becomes slower …
Comprehensible counterfactual explanation on kolmogorov-smirnov test
The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly
detection, astronomy, database security and AI systems. One challenge remained …
detection, astronomy, database security and AI systems. One challenge remained …