Results 1 to 10 of about 31,305 (267)
A problem-agnostic approach to feature selection and analysis using SHAP
Journal of Big DataFeature selection is an effective data reduction technique. SHapley Additive exPlanations (SHAP) can be used to provide a feature importance ranking for models built with labeled or unlabeled data. Thus, one may use the SHAP feature importance ranking in
, Qianxin Liang, Hancock John T
exaly +3 more sources
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2023 Objective: An interpretability model of enterprise digital transformation that integrates XGBoost and Shapley additive explanations (SHAP) is proposed to accurately identify the important factors that affect the digital transformation of enterprises and their mode of action, improve the digital capabilities and levels of enterprises, and prevent the ...
Dan Tang, Jiangying Wei
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Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data
Sensors, 2021 In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not ...
Donghyun Kim +4 more
doaj +1 more source
Ekonomi, Politika & Finans Araştırmaları DergisiThe main objective of this study is to contribute to the literature by forecasting green bond index with different machine learning models supported by artificial intelligence.
Ahmed İhsan Şimşek +2 more
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Calibration, Optimization, and Evaluation of the Integrated NSGA-II and XGBoost Algorithm for Predicting Corn (Zea mays L.) Yield Performance under the Influence of Biofertilizers: A Novel Approach in Low-Input Agriculture [PDF]
بوم شناسی کشاورزیIntroductionAccurate prediction of maize (Zea mays L.) grain yield is critical for efficient resource management and enhancing productivity in sustainable agriculture, particularly in low-input systems.
Mohsen Jahan, Mehdi Nassiri Mahallati
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Xin yixueObjectiveTo explore the value of machine learning-based serological markers in predicting irreversible transmural intestinal necrosis (ITIN) in surgical patients with adhesive small bowel obstruction (ASBO).
Ruming LIU, Youlong ZHU, Jiawei FENG
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A comparative analysis of machine learning models in SHAP analysis
CoRR17 pages, 16 figures, 4 ...
Justin Lin, Julia Fukuyama
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Machine Learning Interpretability in Diabetes Risk Assessment: A SHAP Analysis [PDF]
Computers and Electronics in MedicineDiabetes continues to be a complicated and prevalent metabolic illness, providing a serious burden to public health. While machine learning approaches like extreme gradient boosting (XGBoost) provide intriguing options for diabetes prediction, their 'black-box' nature typically limits clinical interpretability.
Mustafa Kutlu +2 more
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Verifying explainability of a deep learning tissue classifier trained on RNA-seq data
Scientific Reports, 2021 For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions.
Melvyn Yap +11 more
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Adjustment of the Grace Score and Shap Analysis in Stemi Patients
Computer Methods and Programs in BiomedicineThe GRACE (Global Registry of Acute Coronary Events) risk score is a well-established tool for predicting major cardiovascular events in patients with acute coronary syndrome. However, its application in acute ST-segment elevation myocardial infarction (STEMI) requires refinement to enhance its predictive accuracy in clinical settings.In this study, we
Jin Cao +3 more
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