16 papers · ranked by Valyu relevance
Fatma Hilal Yagin, Radwa El Shawi, Abdulmohsen Algarni, Cemil Colak + 3 more
'Fahaid Al-Hashem' 'Luca Paolo Ardigò' 'Gemma Piella'] Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis…
Abirami Gurushanker, A. Jeffrey Rufus, C. Christopher Columbus, C. K. Aravind
The rapid development of smart cities, fueled by the growth of the Internet of Things (IoT) and interconnected systems, has greatly enhanced urban infrastructure, especially in transportation and energy management. However, this increased connectivity also raises the risk of cyberattacks, threatening service…
Mircea Focsa, Eiji Kawamoto, Zheng Peng, Mingwei Zhang + 3 more
'Yunzhang Cheng' 'Tianyi Zhang'] Background The development of sepsis in the intensive care unit (ICU) is rapid, the golden rescue time is short, and the effective way to reduce mortality is rapid diagnosis and early warning. Therefore, real-time prediction models play a key role in the clinical diagnosis and…
Chi Han, Mingyu Jin, Fuying Dong, Pengchong Xu + 6 more
Learning for Evaluating Nanogenerators’ Structural Design Authors: ['Chi Han' 'Mingyu Jin' 'Fuying Dong' 'Pengchong Xu' 'Xinnian Jiang' 'Sheling T. Cai' 'Yuanwen Jiang' 'Yongfeng Zhang' 'Yin Fang' 'Simiao Niu'] The limited battery life in modern mobile, wearable, and implantable electronics critically constrains their…
Rory Mitchell, Eibe Frank, Geoffrey Holmes, Alberto Cano
SHapley Additive exPlanation (SHAP) values ([24]) provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values ([35]). While exact calculation of SHAP values is computationally intractable in general, a recursive polynomial-time algorithm called TreeShap ([23]) is…
Mufeng Chen, Fuchang Luo, Jia Xie, Quansheng Ren + 3 more
Acute respiratory distress syndrome (ARDS) is associated with mortality rates up to 46% and remains challenging to diagnose early due to overlapping clinical presentations. We propose a dual-system framework for multimodal ARDS diagnosis that integrates a Mamba-Bi-LSTM primary discrimination system with a…
Biao Wang, Xia Han, Maoxing Dong, Hui Zhang + 6 more
Objective The COVID-19 pandemic has profoundly altered global influenza circulation. This study aimed to investigate the impact of meteorological factors on influenza transmission in Jiuquan, China, during three distinct phases: before, during, and after the COVID-19 pandemic. Methods Weekly influenza surveillance and…
Iqbal Madakkatel, Elina Hyppönen
Background Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley…
Hendrik Blockeel, Laurens Devos, Benoît Frénay, Géraldin Nanfack + 1 more
'Siegfried Nijssen'] This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which the research is situated, and summarizes strengths and…
Shan Gao, Yunpeng Ma, Heming Jia
The combustion optimization problem of the circulation fluidized bed boiler is regarded as a difficult multi-objective optimization problem that requires simultaneously improving the boiler thermal efficiency and reducing the NOx emissions concentration. In order to solve the above-mentioned problem, a new…
Trang T Le, Jason H Moore, Jonathan Wren
In this article, we presented a new type of integrated visualization of decision trees and heatmaps, which provides a comprehensive data overview as well as model interpretation. We demonstrated that this integration uncovers meaningful patterns among the predictive features and highlights the important elements of…
Grigorios Tzionis, Georgia Kougka, Ilias Gialampoukidis, Stefanos Vrochidis + 2 more
This paper addresses the critical instability of Local Interpretable Model-agnostic Explanations (LIME). We introduce Adaptive Kernel Density Estimation LIME (AKDE-LIME), a novel approach that enhances local explanation stability by incorporating a density-aware weighting scheme. Unlike LIME’s standard proximity…
Guangyi Zhang, Aristides Gionis
Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model interpretability deteriorates. Traditional tree-induction algorithms, such as C4.5 and CART, rely on impurity-reduction functions that promote the discriminative power of each…
Sabino Francesco Roselli, Eibe Frank
Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems. In contrast to “classic” decision trees with constant values in their leaves, model trees can use linear combinations of predictor variables in their leaf nodes to form predictions, which can…
Sungbum Jun, Vassilis Plagianakos
Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need…
Ashwini Venkatasubramaniam, Julian Wolfson, Nathan Mitchell, Timothy Barnes + 2 more
'Timothy Barnes' 'Meghan JaKa' 'Simone French'] Background In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However…