Results 11 to 20 of about 88,634 (286)

An interpretable machine learning model for predicting cavity water depth and cavity length based on XGBoost–SHAP

Journal of Hydroinformatics, 2023
In contrast to the traditional black box machine learning model, the white box model can achieve higher prediction accuracy and accurately evaluate and explain the prediction results.
Shanshan Li
exaly   +3 more sources

Early Type 2 diabetes risk prediction using explainable machine learning in a two-stage approach [PDF]

Frontiers in Digital Health
BackgroundDiabetes is a chronic disease characterized by elevated blood glucose levels. Without early detection and proper management, it can lead to serious complications and increase healthcare costs.
Silas Majyambere, Silas Majyambere, Tony Lindgren, Celestin Twizere, Isaac Ntakirutimana   +4 more
doaj   +2 more sources

Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

Frontiers in Earth Science, 2023
The shear wave velocity (Vs) is significant for quantitative seismic interpretation. Although numerous studies have proved the effectiveness of the machine learning method in estimating the Vs using well-logging parameters, the real-world application is ...
Tianze Zhang, Hui Chai, Hongjun Wang, Tongcui Guo, Liangjie Zhang, Wenqi Zhang   +5 more
doaj   +1 more source

Combining conventional ultrasound and ultrasound elastography to predict HER2 status in patients with breast cancer

Frontiers in Physiology, 2023
Introduction: Identifying the HER2 status of breast cancer patients is important for treatment options. Previous studies have shown that ultrasound features are closely related to the subtype of breast cancer.Methods: In this study, we used features of ...
Xiaoying Zhuo, Xiaoying Zhuo, Ji Lv, Ji Lv, Binjie Chen, Jia Liu, Yujie Luo, Jie Liu, Xiaowei Xie, Jiao Lu, Ningjun Zhao, Ningjun Zhao   +11 more
doaj   +1 more source

On the Tractability of SHAP Explanations

Journal of Artificial Intelligence Research, 2021
SHAP explanations are a popular feature-attribution mechanism for explainable AI. They use game-theoretic notions to measure the influence of individual features on the prediction of a machine learning model. Despite a lot of recent interest from both academia and industry, it is not known whether SHAP explanations of common machine learning ...
Guy Van den Broeck, Anton Lykov, Maximilian Schleich, Dan Suciu   +3 more
openaire   +2 more sources

Ensembles of Random SHAPs

Algorithms, 2022
The ensemble-based modifications of the well-known SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model are proposed. The modifications aim to simplify the SHAP which is computationally expensive when there is a large number of features.
Lev V. Utkin, Andrei V. Konstantinov
openaire   +3 more sources

A Model-agnostic XAI Approach for Developing Low-cost IoT Intrusion Detection Dataset

Journal of Information Security and Cybercrimes Research, 2023
This study tackles the significant challenge of generating low-cost intrusion detection datasets for Internet of Things (IoT) camera devices, particularly for financially limited organizations.
Enoch Opanin Gyamfi, Zhiguang Qin, Daniel Adu-Gyamfi, Juliana Mantebea Danso, Judith Ayekai Browne, Dominic Kwasi Adom, Francis Effirim Botchey, Nelson Opoku-Mensah   +7 more
doaj   +1 more source

Conditional Expectation Network for SHAP

SSRN Electronic Journal, 2023
A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expectation version and an unconditional expectation version (the latter is also known as interventional SHAP).
Ronald Richman, Mario V. Wüthrich
openaire   +2 more sources

A Protocol for Continual Explanation of SHAP

ESANN 2023 proceesdings, 2023
ESANN 2023, 6 pages, added link to ...
Cossu, Andrea, Spinnato, Francesco, Guidotti, Riccardo, Bacciu, Davide   +3 more
openaire   +3 more sources

Prediction and analysis model for ground peak acceleration based on XGBoost and SHAP

Yantu gongcheng xuebao, 2023
In order to establish a prediction method for the ground peak acceleration (PGA) that does not depend on the soil constitutive model but only on the ground motion and site characteristics, six characteristic parameters are chosen through the feature ...
QI Wanwan 1, 2, SUN Rui 1, 2, ZHENG Tong 1, 2, QI Jinlei 1, 2
doaj   +1 more source