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25 papers · ranked by Valyu relevance
Zixin Shi, Linjun Huang, Xiaomei Xu, Kexue Pu + 3 more
'Haolin Wang' 'Arriel Benis'] Title: Abstract Background Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However…
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti, Tsang Ing Ren
'Tsang Ing Ren'] Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of a base classifier, such…
Meizhu Li, Shaoguang Huang, Jasper De Bock, Gert de Cooman + 1 more
'Aleksandra Pižurica'] Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors…
Mohammad Fili, Guiping Hu, Changze Han, Alexa Kort + 2 more
Therapeutics that target the envelope glycoproteins (Envs) of human immunodeficiency virus type 1 (HIV-1) effectively reduce virus levels in patients. However, due to mutations, new Env variants are frequently generated, which may be resistant to the treatments. The appearance of such sequence variance at any Env…
Meinolf Sellmann, Tapan Shah
We consider the dynamic classifier selection (DCS) problem: Given an ensemble of classifiers, we are to choose which classifier to use depending on the particular input vector that we get to classify. The problem is a special case of the general algorithm selection problem where we have multiple different algorithms we…
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
—Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have…
Dongxue Zhao, Xin Wang, Yashuang Mu, Lidong Wang + 1 more
Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were…
Rafael M. O. Cruz, George D. C. Cavalcanti, Tsang Ing Ren
— Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pattern and analyzing the competence of the classifiers in this region. However, the regions are often surrounded by noise which can…
Rafael M. O. Cruz, Hiba H. Zakane, Robert Sabourin, George D. C. Cavalcanti
'George D. C. Cavalcanti'] Abstract— Multiple classifier systems focus on the combination of classifiers to obtain better performance than a single robust one. These systems unfold three major phases: pool generation, selection and integration. One of the most promising MCS approaches is Dynamic Selection (DS), which…
Mariana A. Souza, George D. C. Cavalcanti, Rafael M. O. Cruz, Robert Sabourin
'Robert Sabourin'] Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely only on local data to estimate a classifiers competence, the manner in which the pool is generated could affect the…
Mahsan Abdoli, Mohammad Akbari, Jamal Shahrabi
Automatic credit scoring, which assesses the probability of default by loan applicants, plays a vital role in peer-to-peer lending platforms to reduce the risk of lenders. Although it has been demonstrated that dynamic selection techniques are effective for classification tasks, the performance of these techniques for…
Nasrin Ostvar, Amir Masoud Eftekhari Moghadam
In recent years, ensemble classification methods have been widely investigated in both industry and literature in the field of machine learning and artificial intelligence. The main advantage of this approach is to benefit from a set of classifiers instead of using a single classifier with the aim of improving the…
Martin Sarnovsky, Michal Kolarik, Alberto Cano
Data streams can be defined as the continuous stream of data coming from different sources and in different forms. Streams are often very dynamic, and its underlying structure usually changes over time, which may result to a phenomenon called concept drift. When solving predictive problems using the streaming data…
Shibo Qiu
Genomic Language Models (GLMs), which learn from nucleotide sequences, have become essential tools for understanding the principles of life and have demonstrated outstanding performance in downstream tasks of genomic analysis, such as sequence generation and sequence classification. However, models that achieve…
Hadi Habibzadeh, James J. S. Norton, Theresa M. Vaughan, Tolga Soyata + 1 more
'Tolga Soyata' 'Daphney-Stavroula Zois'] We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction…
Kynon JM Benjamin, Tarun Katipalli, Apuã CM Paquola
Technology advances have generated larger omics datasets with applications for machine learning. Even so, in many datasets, the number of measured features greatly exceeds the number of observations or experimental samples. Dynamic recursive feature elimination (RFE) provides a flexible feature elimination framework to…
Océane Fourquet, Martin S. Krejca, Carola Doerr, Benno Schwikowski
Monotonic bivariate classifiers can describe simple patterns in high-dimensional data that may not be discernible using only elementary linear decision boundaries. Such classifiers are relatively simple, easy to interpret, and do not require large amounts of data to be effective. A challenge is that finding optimal…
Authors not listed
Developing generalizable machine learning models with minimal data remains a central challenge in materials informatics. Effective models can significantly reduce costly computational simulations and time-intensive experimentation by providing reliable predictions of material properties. In this work, we investigate…
Melania Nowicka, Heike Siebert
Cell classifiers are decision-making synthetic circuits that allow in vivo cell-type classification. Their design is based on finding a relationship between differential expression of miRNAs and the cell condition. Such biological devices have shown potential to become a valuable tool in cancer treatment as a new…
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…
Patrick J. Trainor, Andrew P. DeFilippis, Shesh N. Rai
Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes and for furnishing diagnostic and prognostic phenotype predictions in medicine. Despite this, a comprehensive and rigorous evaluation of classification techniques for phenotype…
Kunal Lodaya, Nathan Ricke, Kelly Chen, Troy Van Voorhis
Graphite-conjugated catalysts (GCCs) are a promising class of materials that combine many of the advantages of heterogenous and homogeneous catalysts. In particular, GCCs containing an aryl-pyridinium active site appear to be effective nonmetal catalysts for the oxygen reduction reaction (ORR). In this study, we…
Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter + 1 more
There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation of generative models remains challenging and suggested…
Authors not listed
Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…