29 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…
Cesar Alfaro, Javier Gomez, Javier M. Moguerza, Javier Castillo + 2 more
'Jose I. Martinez' 'Sotiris Kotsiantis'] Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy…
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…
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…
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…
DongSeong-Yoon
—Since the Fourth Industrial Revolution, AI technology has been widely used in many fields, but there are several limitations that need to be overcome, including overfitting/underfitting, class imbalance, and the limitations of representation (hypothesis space) due to the characteristics of different models. As a…
Arjun Pakrashi, Brian Mac Namee
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by…
Shikun Chen, Wenlong Zheng, Syed Nisar Hussain Bukhari,
Ensemble regression methods are widely used to improve prediction accuracy by combining multiple regression models, especially when dealing with continuous numerical targets. However, most ensemble voting regressors use equal weights for each base model’s predictions, which can limit their effectiveness, particularly…
Tasnimul Hasan, Md. Faiyed Bin Karim, Mahin Khan Mahadi, Mirza Muntasir Nishat + 1 more
'Mirza Muntasir Nishat' 'Fahim Faisal'] The endeavor to detect human activities and behaviors is targeted as a real-time detection mechanism that tends to predict the form of human motions and actions. Though sensors like accelerometer and gyroscopes are noticeable in human motion detection, categorizing unique and…
Alexios Chatzigoulas, Zoe Cournia
Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this…
Michael Onyema Edeh, Surjeet Dalal, Imed Ben Dhaou, Charles Chuka Agubosim + 3 more
Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8…
Giang Ngo, Rodney Beard, Rohitash Chandra
Ensemble learning has gained success in machine learning with major advantages over other learning methods. Bagging is a prominent ensemble learning method that creates subgroups of data, known as bags, that are trained by individual machine learning methods such as decision trees. Random forest is a prominent example…
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…
Wenjing Li, Randy Paffenroth, David Berthiaume
Ensemble learning is a process by which multiple base learners are strategically generated and combined into one composite learner. There are two features that are essential to an ensemble's performance, the individual accuracies of the component learners and the overall diversity in the ensemble. The right balance of…
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…
Moshe Sipper
> Abstract. We present Classy Ensemble, a novel ensemble-generation algorithm for classification tasks, which aggregates models through a weighted combination of per-class accuracy. Tested over 153 machine learning datasets we demonstrate that Classy Ensemble outperforms two other well-known aggregation…
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…
Jason J. Yoo, Tony Joseph, Dylan Yung, S. Ali Nasseri + 1 more
There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the trickiness of hyperparameter tuning. Such barriers have led to an explosion of interest in automated machine learning (AutoML), whereby an…
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…
Authors not listed
Background: Batch reactor process optimization has traditionally relied on Analysis of Variance (ANOVA) for factor effect quantification. However, Structural Equation Modeling (SEM) and machine learning (ML) offer complementary mechanistic and predictive capabilities that remain underexplored in chemical engineering…
Muhammad Noman, Maria Hanif, Abdul Hameed, Muhammad Babar + 2 more
Diabetes mellitus presents a significant global health challenge, particularly in regions like Pakistan, India, and Bangladesh. Machine learning (ML) techniques offer promising solutions for diabetes prediction, surpassing traditional methods in reliability and efficiency. This research conducts a comparative analysis…
Tomasz Konopka
Machine learning models in bioinformatics are often trained and used within the scope of a single project, but some models are also reused across projects and deployed in translational settings. Over time, trained models may turn out to be maladjusted to the properties of new data. This creates the need to improve…
Moayad Alnammi, Shengchao Liu, Spencer S Ericksen, Gene E Ananiev + 6 more
Traditional small molecule drug discovery is a time consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine learning methods for virtual chemical screening enables training models on known active and…
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.…
Authors not listed
In molecular machine learning, the choice of the representation of molecules can have a significant impact on model performance. However, understanding the root causes of these performance differences often proves challenging. One promising approach to explore model behavior is representational alignment, which…
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…
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
Jenke Scheen, Wilson Wu, Antonia S. J. S. Mey, Paolo Tosco + 2 more
A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database, and retrospectively shown to outperform most submissions from the…