27 papers · ranked by Valyu relevance
Palak Mahajan, Shahadat Uddin, Farshid Hajati, Mohammad Ali Moni + 1 more
'Joaquim Carreras'] Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have…
Victor Chukwudi Osamor, Adaugo Fiona Okezie
Tuberculosis has the most considerable death rate among diseases caused by a single micro-organism type. The disease is a significant issue for most third-world countries due to poor diagnosis and treatment potentials. Early diagnosis of tuberculosis is the most effective way of managing the disease in patients to…
Vanda M. Lourenço, Joseph O. Ogutu, Rui A.P. Rodrigues, Hans-Peter Piepho
The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not…
Sophie A. Murray
The space weather community has begun to use frontier methods such as data assimilation, machine learning, and ensemble modeling to advance current operational forecasting efforts. This was highlighted by a multi-disciplinary session at the 2017 American Geophysical Union Meeting, 'Frontier Solar-Terrestrial Science…
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…
Pierre‐Alexandre Mattei, Damien Garreau
Ensemble methods combine the predictions of several base models. We study whether or not including more models always improves their average performance. This question depends on the kind of ensemble considered, as well as the predictive metric chosen. We focus on situations where all members of the ensemble are a…
Li Shandross, Emily Howerton, Lucie Contamin, Harry Hochheiser + 3 more
Combining predictions from multiple models into an ensemble is a widely used practice across many fields with demonstrated performance benefits. Popularized through domains such as weather forecasting and climate modeling, multi-model ensembles are becoming increasingly common in public health and biological…
Li Shandross, Emily Howerton, Lucie Contamin, Harry Hochheiser + 20 more
Combining predictions from multiple models into an ensemble is a widely used practice across many fields with demonstrated performance benefits. Popularized through domains such as weather forecasting and climate modeling, multi-model ensembles are becoming increasingly common in public health and biological…
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…
Akhlaqur Rahman, Sumaira Tasnim
Ensemble classifier refers to a group of individual classifiers that are cooperatively trained on data set in a supervised classification problem. In this paper we present a review of commonly used ensemble classifiers in the literature. Some ensemble classifiers are also developed targeting specific applications. We…
Kaveh Faraji Googerdchi, Shahrokh Asadi, Seyed Mohammadbagher Jafari, Sajid Anwar
'Sajid Anwar'] Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble classifiers. Therefore, developing accurate ensemble…
João Mendes‐Moreira, Tiago Mendes-Neves
Ensemble learning has been a focal point of machine learning research due to its potential to improve predictive performance. This study revisits the foundational work on ensemble error decomposition, historically confined to bias-variance-covariance analysis for regression problems since the 1990s. Recent advancements…
Grzegorz Dudek
Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast complex time series, we propose ensemble learning which is based on randomized neural networks, and boosted in three ways. These comprise…
Sebastian Spänig, Alexander Michel, Dominik Heider
Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever-growing amount of annotated peptides, researchers employed artificial intelligence to…
Valdigleis S. Costa, Antonio Diego Silva Farias, Benjamín Bedregal, Regivan Santiago + 1 more
'Regivan Santiago' 'Anne M. P. Canuto'] Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate…
Jay Devine, Helen K. Kurki, Jonathan R. Epp, Paula N. Gonzalez + 2 more
Classification is a fundamental task in biology used to assign members to a class. While linear discriminant functions have long been effective, advances in phenotypic data collection are yielding increasingly high-dimensional datasets with more classes, unequal class covariances, and non-linear distributions. Numerous…
Kuo-Wei Hsu
Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The…
Maryam Sabzevari, Gonzalo Martínez-Muñoz, Alberto Suárez
—In ensemble methods, the outputs of a collection of diverse classifiers are combined in the expectation that the global prediction be more accurate than the individual ones. Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Sergey V. Kovalchuk, Aleksey V. Krikunov, Konstantin V. Knyazkov, Alexander V. Boukhanovsky
'Alexander V. Boukhanovsky'] Contemporary tasks of complex system simulation are often related to the issue of uncertainty management. It comes from the lack of information or knowledge about the simulated system as well as from restrictions of the model set being used. One of the powerful tools for the uncertainty…
Maya Ramchandran, Prasad Patil, Giovanni Parmigiani
Multi-study learning uses multiple training studies, separately trains classifiers on individual studies, and then forms ensembles with weights rewarding members with better cross-study prediction ability. This article considers novel weighting approaches for constructing tree-based ensemble learners in this setting.…
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
Rubén Herzog, Arturo Morales, Soraya Mora, Joaquin Araya + 3 more
We propose a novel, scalable, and accurate automated method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It allows the participation of neurons in different ensembles, has few parameters to tune and is computationally…
Murat Cihan Sorkun, Elham Nour Ghassemi, Cihan Yatbaz, J. M. Vianney A. Koelman + 1 more
Aqueous Organic Redox Flow Batteries (AORFBs) are considered as one of the most appealing technologies for large-scale energy storage due to their electroactive organic materials, which are abundant, easy to produce, and recyclable. A prevailing challenge for the redox chemistries applied in AORFBs is to achieve high…
Susanta Das, Kenneth M. Merz, Jr.
Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and identifying energetically…
Nathanael J. King, Ian LeBlanc, Alex Brown
Conformational ensemble generation and the search for the global minimum conformation are important problems in computational chemistry. In this work, a variant on the Conformer-Rotamer Ensemble Sampling Tool (CREST) algorithm designed for determining structural ensembles and energetics of non-covalent clusters of…
Stamatia Zavitsanou, Zonghua Bo, Emanuele Casali, Matthew Langton + 1 more
Machine learning (ML) is currently transforming the field of chemistry by offering unparalleled efficiency in addressing complex challenges. Despite the progress made, a notable gap persists in the availability of user-friendly tools tailored to chemical problems involving small and sparse datasets. Here, we introduce…