Results 71 to 80 of about 88,634 (286)

Shap-Select: Lightweight Feature Selection Using SHAP Values and Regression

CoRR
Feature selection is an essential process in machine learning, especially when dealing with high-dimensional datasets. It helps reduce the complexity of machine learning models, improve performance, mitigate overfitting, and decrease computation time. This paper presents a novel feature selection framework, shap-select.
Egor Kraev, Baran Koseoglu, Luca Traverso, Mohammed Topiwalla   +3 more
openaire   +2 more sources

Integrating Machine Learning With Constant‐Potential Simulation to Unravel Charge‐Transfer Mechanisms in Electrochemical Nitrogen Fixation

Advanced Science, EarlyView.
Integrating interpretable machine learning with the fixed‐potential method reveals a novel mechanism: the catalytic activity of the electrochemical nitrogen reduction reaction is governed by partial charge transfer, induced by variations in the intermediate potential of zero charge under constant potential.
Yufei Xue, Dushuo Feng, Yuefei Zhang, Yang Zhang, Yalong Jiao, Aijun Du, Guoping Gao   +6 more
wiley   +1 more source

Personal bankruptcy prediction using machine learning techniques

Economics and Business Review
It has become crucial to have an early prediction model that provides accurate assurance for users about the financial situation of consumers. Recent studies have focused on predicting corporate bankruptcies and credit defaults, not personal bankruptcies.
Brygała Magdalena, Korol Tomasz
doaj   +1 more source

Explainable machine learning for predicting coronary heart disease risk in patients with carotid atherosclerosis: A retrospective study with SHAP and decision curve analysis

Journal of Clinical and Translational Science
Background: Carotid atherosclerosis is associated with increased coronary heart disease (CHD) risk, yet current risk models lack specificity and interpretability for this population. This study aimed to develop explainable machine learning (ML) models to
Lei Zhang, Mengke Lyu, Mingyuan Du, Yizhuo Li, Haifeng Yan, Xiaohui Li, Wenshuang Niu, Lizhi Pang   +7 more
doaj   +1 more source

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Sector-specific financial forecasting with machine learning algorithm and SHAP interaction values

Financial Internet Quarterly
This study examines the application of machine learning models to predict financial performance in various sectors, using data from 21 companies listed in the BIST100 index (2013-2023).
Ergenç Cansu, Aktaş Rafet
doaj   +1 more source

Analysis of Gradient Boosting Algorithms with Optuna Optimization and SHAP Interpretation for Phishing Website Detection

Journal of Applied Informatics and Computing
Phishing remains a persistent cybersecurity threat, evolving rapidly to bypass traditional blacklist-based detection systems. Machine Learning (ML) approaches offer a promising solution, yet finding the optimal balance between detection accuracy and ...
Rahmat Fauzi Abu Bakar, Majid Rahardi
doaj   +1 more source

OSL Characterisation of Two Fluvial Sequences of the River Usmacinta in its Middle Catchment (SE Mexico) [PDF]

, 2014
The report summarizes luminescence profiling, initially using a SUERC PPSL system in Mexico, and laboratory analysis at SUERC, used to characterise the stratigraphy and interpret sedimentary processes in terrace deposits of the Usumacinta River, SE ...
Castillo Rodriguez, Miguel, Cruz-Zaragoza, Epifanio, Kinnaird, Tim C., Muñoz-Salinas, Esperanza, Sanderson, David C.W.   +4 more
core  

From Cell‐Free Transcriptomes to Single‐Cell Landscapes: Biomarker Discovery and Originating Cell Alteration Analysis via Graph Matrix Factorization

Advanced Science, EarlyView.
CellFreeGMF traces plasma cfRNA to likely originating cell types by integrating single‐cell atlases with graph‐regularized matrix factorization. The method decomposes cfRNA profiles into sample–cell contributions to reconstruct pseudo single‐cell expression.
Wenxiang Zhang, Wenjing Zhang, Hang Wei, Shiyan Liu, Junliang Shang, Weijie Gong, Hanwen Cheng, Xiujuan Lei, Yuhui Kou, Baoguo Jiang   +9 more
wiley   +1 more source