27 papers · ranked by Valyu relevance
Y. Coadou
Boosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the highenergy physics context and describing ways to quantify the performance and training quality of classifiers, decision trees are described. Some of their shortcomings are then…
Deepti Rani, Nasib Singh Gill, Preeti Gulia, Jyotir Moy Chatterjee
Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and…
Ebenezer Owusu, Prince Boakye-Sekyerehene, Justice Kwame Appati, Julius Yaw Ludu
'Julius Yaw Ludu'] Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the…
Sarah E. Lindley, Yiyang Lu, Diwakar Shukla
Guide to Machine Learning for Small Molecule Design Authors: ['Sarah\nE. Lindley' 'Yiyang Lu' 'Diwakar Shukla'] Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML…
Riccardo De Bin, Vegard Grødem Stikbakke
In this paper we propose a boosting algorithm to extend the applicability of a first hitting time model to high-dimensional frameworks. Based on an underlying stochastic process, first hitting time models do not require the proportional hazards assumption, hardly verifiable in the high-dimensional context, and…
Ping Li, Weijie Zhao
This report presents the open-source package https://github.com/pltrees/abcboost which implements the series of boosting works over the past many years (Li, 2008, 2009, 2010a,b; Li and Zhao, 2022a,c). In particular, this package includes mainly three lines of techniques, among which the following two techniques are…
Mikael Møller Høgsgaard, Kasper Green Larsen, Markus Engelund Mathiasen
'Markus Engelund Mathiasen'] Boosting is an extremely successful idea, allowing one to combine multiple low accuracy classifiers into a much more accurate voting classifier. In this work, we present a new and surprisingly simple Boosting algorithm that obtains a provably optimal sample complexity. Sample optimal…
Jiming Liu, Dongjin Xu
Lithology is a key parameter in reservoir fine description and evaluation. It is difficult to directly identify reservoir lithology using a single logging curve or conventional cross-plot method due to the mud-gravel mixing in complex reservoirs. The accurate identification of conglomerate reservoir lithology has…
Ivan Malashin, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub + 2 more
'Aleksei Borodulin' 'Qingsheng Wang'] The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest in machine learning (ML) methods to aid in data analysis, material design, and predictive modeling. Among the various ML approaches, boosting methods…
Suneel Babu Chatla
We investigate L2 boosting in the context of kernel regression. Kernel smoothers, in general, lack appealing traits like symmetry and positive definiteness, which are critical not only for understanding theoretical aspects but also for achieving good practical performance. We consider a projection-based smoother (Huang…
Hagar F. Gouda, Fatma D. M. Abdallah
Ensemble machine learning (ML) algorithms, such as bagging and boosting, are powerful decision-support tools that enhance disease prediction and risk management in the veterinary field. Lumpy Skin Disease (LSD) poses a significant threat to livestock health and results in substantial economic losses. This study aims to…
Richard Nock, Yishay Mansour
Boosting is a highly successful ML-born optimization setting in which one is required to computationally efficiently learn arbitrarily good models based on the access to a weak learner oracle, providing classifiers performing at least slightly differently from random guessing. A key difference with gradient-based…
Paul Liautaud, Pierre Gaillard, Olivier Wintenberger
We study boosting for adversarial online nonparametric regression with general convex losses. We first introduce a parameter-free online gradient boosting (OGB) algorithm and show that its application to chaining trees achieves minimax optimal regret when competing against Lipschitz functions. While competing with…
Jian Qian, Shu Ge
We formalize this stability property as (α, β)-boostability. We show that geometric median aggregation achieves (α, β)-boostability for a broad class of divergences, with tradeoffs that depend on the underlying geometry. For vector-valued prediction and conditional density estimation, we characterize boostability under…
Bibhuprasad Sahu, Amrutanshu Panigrahi, Abhilash Pati, Manmath Nath Das + 4 more
'Manmath Nath Das' 'Prince Jain' 'Ghanashyam Sahoo' 'Haipeng Liu' 'Mohammad Rahimi-Gorji'] Objective: The cancer death rate has accelerated at an alarming rate, making accurate diagnosis at the primary stages crucial to enhance prognosis. This has deepened the issue of cancer mortality, which is already at an…
Hannah Klinkhammer, Christian Staerk, Carlo Maj, Peter M. Krawitz + 1 more
Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in the field of clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To…
Xiaoye Mo, Xia Jiang
Ubiquitination-site prediction is an important task because ubiquitination is a critical regulatory function for many biological processes such as proteasome degradation, DNA repair and transcription, signal transduction, endocytoses, and sorting. However, the highly dynamic and reversible nature of ubiquitination…
Tomohiro Ishibashi, Akio Onogi
Mapping quantitative trait loci (QTLs) is one of the major goals of quantitative genetics; however, identifying the interactions between QTLs remains challenging. Recently developed machine learning methods, such as deep learning and gradient boosting, are transforming the real world. These methods could advance QTL…
Itamar Borges Jr, Júlio César Duarte, Romulo Dias da Rocha
We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, ∑▒〖Q_0 (NO_2)〗 (the charge of the nitro groups)…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
Dániel Sándor, Péter Antal
In multitask federated learning, when small amounts of data are available, it can be harder to achieve proper predictive performance, especially if the clients’ tasks are different. However, task heterogeneity is common in modern Drug-Target interaction (DTI) prediction problems. As the data available for DTI tasks are…
Authors not listed
Hydration free energy (HFE) of molecules is a fundamental property having impor- tance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Ma- chine learning (ML) models are increasingly being used to predict HFE.…
William Manley, Tam Tran, Melissa Prusinski, Dustin Brisson
General linear models have been the foundational statistical framework used to discover the ecological processes that explain the distribution and abundance of natural populations. Analyses of the rapidly expanding cache of environmental and ecological data, however, require advanced statistical methods to contend with…
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
Authors not listed
Background: Janus Kinase 2 (JAK2) is a key kinase in cellular signal transduction. Its abnormal activation is closely related to various myeloproliferative neoplasms and inflammatory diseases. Developing selective JAK2 inhibitors is an important direction in drug discovery. Accurate prediction of compound inhibitory…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Feng Feng, Zhenru Chen, Jianyuan Ni, Yuanxun Zhang + 3 more
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is…