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24 papers · ranked by Valyu relevance

01
AarXiv2022
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity
Claus Metzner, Achim Schilling, Maximilian Traxdorf, Konstantin Tziridis + 2 more
'Konstantin Tziridis' 'Holger Schulze' 'Patrick Krauß'] Data classification, the process of analyzing data and organizing it into categories, is a fundamental computing problem of natural and artificial information processing systems. Ideally, the performance of classifier models would be evaluated using unambiguous…
87relevance
02
PPubMed4 Oct 2023
Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient Classification accuracy assessment in an imperfect world
Giles M. Foody, Shigao Huang
The accuracy of a classification is fundamental to its interpretation, use and ultimately decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the true accuracy. Mis-estimation of classification accuracy metrics and associated mis-interpretations are often due to variations in…
87relevance
03
AarXiv4 Jul 2025
Absolute Evaluation Measures for Machine Learning: A Survey
Silvia Beddar-Wiesing, Alice Moallemy-Oureh, Marie Kempkes, Josephine M. Thomas
Machine Learning is a diverse field applied across various domains such as computer science, social sciences, medicine, chemistry, and finance. This diversity results in varied evaluation approaches, making it difficult to compare models effectively. Absolute evaluation measures offer a practical solution by assessing…
81relevance
04
PPubMed10 Apr 2022
Preference-Driven Classification Measure
Jan Kozak, Barbara Probierz, Krzysztof Kania, Przemysław Juszczuk + 1 more
Classification is one of the main problems of machine learning, and assessing the quality of classification is one of the most topical tasks, all the more difficult as it depends on many factors. Many different measures have been proposed to assess the quality of the classification, often depending on the application…
80relevance
05
PPubMed10 Jul 2025
The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review
Wilhelm Grzesiak, Daniel Zaborski, Marcin Pluciński, Magdalena Jędrzejczak-Silicka + 3 more
'Magdalena Jędrzejczak-Silicka' 'Renata Pilarczyk' 'Piotr Sablik' 'Andrea Pezzuolo'] Title: Simple Summary The current trend in animal husbandry, including cattle farming, is toward increasing stocking density and automating individual activities in animal care. Various electro-optical, acoustic, mechanical, and…
80relevance
06
PPubMed14 Feb 2023
Common statistical concepts in the supervised Machine Learning arena
Hooman H. Rashidi, Samer Albahra, Scott Robertson, Nam K. Tran + 1 more
'Bo Hu'] One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be…
79relevance
07
BbioRxiv25 Aug 2026
Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches
Areen Arabiat, Hamza Abu Owida, Suhaila Abuowaida, Nawaf Alshdaifat + 2 more
This study emphasizes the potential of computational techniques in cancer risk assessment, highlighting opportunities for specific and data-driven healthcare solutions. It examines the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches to improve cancer risk assessment using a…
78relevance
08
PPubMed14 Nov 2023
An ensemble approach for imbalanced multiclass malware classification using 1D-CNN
Binayak Panda, Sudhanshu Shekhar Bisoyi, Sidhanta Panigrahy, Vicente Alarcon-Aquino
'Vicente Alarcon-Aquino'] Dependence on the internet and computer programs demonstrates the significance of computer programs in our day-to-day lives. Such demands motivate malware developers to create more malware, both in terms of quantity and variety. Researchers are constantly faced with hurdles while attempting to…
77relevance
09
AarXiv13 Dec 2021
Incorporating Measurement Error in Astronomical Object Classification
Sarah Shy, Hyungsuk Tak, Eric D. Feigelson, John Timlin + 1 more
Most general-purpose classification methods, such as support-vector machine (SVM) and random forest (RF), fail to account for an unusual characteristic of astronomical data: known measurement error uncertainties. In astronomical data, this information is often given in the data but discarded because popular machine…
76relevance
10
AarXiv15 Apr 2026
Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Xuanyan Liu, Ignacio Cabrera Martin, Marcello Trovati, Xiaolong Xu + 1 more
The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread availability of machine learning libraries and automated workflows, model assessment is often reduced to the reporting of a small set of aggregate metrics, which can lead to…
75relevance
11
CchemRxiv23 Dec 2025
Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
72relevance
12
CchemRxiv10 Oct 2022
From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization
Saer Samanipour, Jake O'Brien, Malcolm Reid, Kevin Thomas + 1 more
The European Chemicals Agency (ECHA) and US Environmental Protection Agency (EPA) have listed approximately 800k chemicals that must be further investigated for their potential environmental and/or human health risk. A significant number of these chemicals have large enough global volumes of consumption (e.g.…
70relevance
13
AarXiv11 Feb 2025
The Art of Misclassification: Too Many Classes, Not Enough Points
Mario Franco, Gerardo L. Febres, Nelson Fernández, Carlos Gershenson
Classification is a ubiquitous and fundamental problem in artificial intelligence and machine learning, with extensive efforts dedicated to developing more powerful classifiers and larger datasets. However, the classification task is ultimately constrained by the intrinsic properties of datasets, independently of…
69relevance
14
BbioRxiv16 Dec 2025
The reliability of acoustic classification models for determining avian vocalisation patterns
O.C. Metcalf, C. Alencar Nunes, W.A. Hopping, A.C. Lees + 2 more
Automated detection and classification of species vocalisations offers the potential to utilise acoustic datasets across unprecedented spatial and temporal scales. However, classification algorithms inevitably generate errors, and error rates vary with context. While methods for quantifying error rates in ecoacoustics…
69relevance
15
CchemRxiv17 Dec 2025
Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
69relevance
16
BbioRxiv15 Apr 2024
A novel method to guide biomarker combinations to optimize the sensitivity
Seyyed Mahmood Ghasem, Johannes F. Fahrmann, Samir Hanash, Kim-Anh Do + 2 more
Logistic regression has demonstrated its utility in classifying binary labeled datasets through the maximum likelihood approach. However, in numerous biological and clinical contexts, the aim is often to determine coefficients that yield the highest sensitivity at the pre-specified specificity or vice versa. Therefore…
69relevance
17
CchemRxiv26 Dec 2023
One size does not fit all: revising traditional paradigms for QSAR-based virtual screenings.
James Wellnitz, Sankalp Jain, Joshua Hochuli, Travis Maxfield + 3 more
Traditional best practices for Quantitative Structure Activity Relationship (QSAR) modeling recommend dataset balancing and balanced accuracy (BA) as the key desired objective of model development. This study challenges the conventional norms by recommending the use of models with the highest positive predictive value…
68relevance
18
BbioRxiv21 Oct 2024
Moving towards more holistic validation of machine learning-based approaches in ecology and evolution
Charlotte Christensen, André C. Ferreira, Wismer Cherono, Maria Maximiadi + 4 more
Machine-learning (ML) is revolutionizing the study of ecology and evolution, but the performance of models (and their evaluation) is dependent on the quality of the training and validation data. Currently, we have standard metrics for evaluating model performance (e.g., precision, recall, F1), but these to some extent…
68relevance
19
CchemRxiv5 Jun 2023
Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?
Esteban Bertsch Aguilar, Sebastián Suñer Sánchez, Silvana Pinheiro, William J. Zamora Ramírez
- 1. 1. CBio3 Laboratory, School of Chemistry, University of Costa Rica, San Pedro, San José, Costa Rica - 2. 2. Laboratory of Computational Toxicology and Artificial Intelligence (LaToxCIA), Biological Testing Laboratory (LEBi), University of Costa Rica, San Pedro, San José, Costa Rica - 3. 3. Advanced Computing Lab…
67relevance
20
PPubMed20 Mar 2023
Early detection of student degree-level academic performance using educational data mining
Areej Fatemah Meghji, Naeem Ahmed Mahoto, Yousef Asiri, Hani Alshahrani + 3 more
'Hani Alshahrani' 'Adel Sulaiman' 'Asadullah Shaikh' 'Shadi Aljawarneh'] Higher educational institutes generate massive amounts of student data. This data needs to be explored in depth to better understand various facets of student learning behavior. The educational data mining approach has given provisions to extract…
66relevance
21
AarXiv10 Jul 2024
The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others
Daniel Sikar, Artur S. d’Avila Garcez, Robin Bloomfield, Tillman Weyde + 4 more
Be Misclassified Than Others Authors: ['Daniel Sikar' 'Artur S. d’Avila Garcez' 'Robin Bloomfield' 'Tillman Weyde' 'Kaleem Peeroo' 'Naman Singh' 'Maeve Hutchinson' 'Mirela Reljan-Delaney'] This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural…
63relevance
22
BbioRxiv30 Jun 2026
Impacts of batch effects on the performance of machine learning classifiers across multiple studies
Preston Raab, W. Evan Johnson, Stephen R. Piccolo
Precision medicine relies on accurate and generalizable predictions for patients across the spectrum of human diversity. Because capturing biological heterogeneity requires large sample sizes, researchers must often aggregate data from several experimental batches or independent studies. This integration allows for…
62relevance
23
CchemRxiv27 Mar 2024
Machine Learning in Chemistry
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…
62relevance
24
CchemRxiv24 Sep 2025
Towards robust databases: an ensemble-based workflow for error detection applied to chemical data
Authors not listed
This study presents a validation and refinement of the “yellow cards” error detection workflow that can be applied to any property connected to molecular structure. In our implementation the workflow employed 5 predictive models with each assigning a “yellow card” to 5% of the entries with worst prediction accuracy.…
59relevance

Results for Classification accuracy

Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient Classification accuracy assessment in an imperfect world

Absolute Evaluation Measures for Machine Learning: A Survey

Preference-Driven Classification Measure

The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review

Common statistical concepts in the supervised Machine Learning arena

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

An ensemble approach for imbalanced multiclass malware classification using 1D-CNN

Incorporating Measurement Error in Astronomical Object Classification

Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

The Art of Misclassification: Too Many Classes, Not Enough Points

The reliability of acoustic classification models for determining avian vocalisation patterns

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

A novel method to guide biomarker combinations to optimize the sensitivity

One size does not fit all: revising traditional paradigms for QSAR-based virtual screenings.

Moving towards more holistic validation of machine learning-based approaches in ecology and evolution

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Early detection of student degree-level academic performance using educational data mining

The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others

Impacts of batch effects on the performance of machine learning classifiers across multiple studies

Machine Learning in Chemistry

Towards robust databases: an ensemble-based workflow for error detection applied to chemical data

Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity

Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient Classification accuracy assessment in an imperfect world

Absolute Evaluation Measures for Machine Learning: A Survey

Preference-Driven Classification Measure

The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review

Common statistical concepts in the supervised Machine Learning arena

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

An ensemble approach for imbalanced multiclass malware classification using 1D-CNN

Incorporating Measurement Error in Astronomical Object Classification

Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization

The Art of Misclassification: Too Many Classes, Not Enough Points

The reliability of acoustic classification models for determining avian vocalisation patterns

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

A novel method to guide biomarker combinations to optimize the sensitivity

One size does not fit all: revising traditional paradigms for QSAR-based virtual screenings.

Moving towards more holistic validation of machine learning-based approaches in ecology and evolution

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Early detection of student degree-level academic performance using educational data mining

The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others

Impacts of batch effects on the performance of machine learning classifiers across multiple studies

Machine Learning in Chemistry

Towards robust databases: an ensemble-based workflow for error detection applied to chemical data