Search · four archives

Results for classification performance

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

17 papers · ranked by Valyu relevance

01
PPubMed24 Apr 2014
A Systematic Comparison of Supervised Classifiers A Systematic Comparison of Supervised Classifiers
Diego Raphael Amancio, Cesar Henrique Comin, Dalcimar Casanova, Gonzalo Travieso + 4 more
'Gonzalo Travieso' 'Odemir Martinez Bruno' 'Francisco Aparecido Rodrigues' 'Luciano da Fontoura Costa' 'Hong-Bin Shen'] Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long…
87relevance
02
PPubMed24 Nov 2014
A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network A Novel Hybrid Classification Model
Nader Salari, Shamarina Shohaimi, Farid Najafi, Meenakshii Nallappan + 2 more
'Isthrinayagy Karishnarajah' 'Sergio Gómez'] Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of…
84relevance
03
PPubMed29 Aug 2025
Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis
Yeliz Senkaya, Cetin Kurnaz, Ferdi Ozbilgin, Yong-An Chung
Background/Objectives: Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that progressively impairs cognitive, neurological, and behavioral functions, severely affecting quality of life. The current diagnostic process relies on expert interpretation of extensive clinical assessments, often leading to…
84relevance
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…
82relevance
05
PPubMed18 Sep 2019
The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer
Avinash Parnandi, Jasim Uddin, Dawn M. Nilsen, Heidi M. Schambra
Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological…
81relevance
06
PPubMed17 Aug 2020
Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders
Authors not listed
Goal: This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. Methods: First, an iEMG signal is decimated to produce a set of “disjoint”…
81relevance
07
PPubMed24 Jul 2017
Modified Mahalanobis Taguchi System for Imbalance Data Classification
Mahmoud El-Banna
The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing…
80relevance
08
PPubMed29 Feb 2024
Performance analysis of data resampling on class imbalance and classification techniques on multi-omics data for cancer classification Performance analysis of resampling and classification techniques for cancer classification
Yuting Yang, Golrokh Mirzaei, Muhammad Umer
Cancer, in any of its forms, remains a significant public health concern worldwide. Advances in early detection and treatment could lead to a decline in the overall death rate from cancer in recent decades. Therefore, tumor prediction and classification play an important role in fighting cancer. This study built…
80relevance
09
PPubMed11 Dec 2025
Medical pattern classification using a novel binary similarity approach based on an associative classifier
Osvaldo Velazquez-Gonzalez, Antonio Alarcón-Paredes, Cornelio Yañez-Marquez
Classification is a central task in machine learning, underpinning applications in domains such as finance, medicine, engineering, information technology, and biology. However, machine learning pattern classification can become a complex or even inexplicable task for current robust models due to the complexity of…
80relevance
10
PPubMed6 Jan 2017
SVM and SVM Ensembles in Breast Cancer Prediction SVM and SVM Ensembles in Breast Cancer Prediction
Min-Wei Huang, Chih-Wen Chen, Wei-Chao Lin, Shih-Wen Ke + 2 more
'Chih-Fong Tsai' 'Enrique Hernandez-Lemus'] Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector…
79relevance
11
PPubMed19 Aug 2021
The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers
Sung-Cheol Kim, Adith S. Arun, Mehmet Eren Ahsen, Robert Vogel + 1 more
'Gustavo Stolovitzky'] Title: Significance While it would be desirable that the output of binary classification algorithms be the probability that the classification is correct, most algorithms do not provide a method to calculate such a probability. We propose a probabilistic output for binary classifiers based on an…
77relevance
12
PPubMed4 Nov 2013
A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance
Quang Hung Do, Jeng-Fung Chen
Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam…
75relevance
13
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…
75relevance
14
PPubMed19 Apr 2016
Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection
Abdelniser Moomen, Abdulbaset Ali, Omar M. Ramahi, Ferran Martín + 1 more
'Jordi Naqui'] Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing…
69relevance
15
PPubMed27 Apr 2023
Body composition predicts hypertension using machine learning methods: a cohort study
Mohammad Ali Nematollahi, Soodeh Jahangiri, Arefeh Asadollahi, Maryam Salimi + 9 more
'Maryam Salimi' 'Azizallah Dehghan' 'Mina Mashayekh' 'Mohamad Roshanzamir' 'Ghazal Gholamabbas' 'Roohallah Alizadehsani' 'Mehdi Bazrafshan' 'Hanieh Bazrafshan' 'Hamed Bazrafshan drissi' 'Sheikh Mohammed Shariful Islam'] We used machine learning methods to investigate if body composition indices predict hypertension.…
68relevance
16
PPubMed26 Mar 2024
A comparative study in class imbalance mitigation when working with physiological signals
Rawan S. Abdulsadig, Esther Rodriguez-Villegas
Class imbalance is a common challenge that is often faced when dealing with classification tasks aiming to detect medical events that are particularly infrequent. Apnoea is an example of such events. This challenge can however be mitigated using class rebalancing algorithms. This work investigated 10 widely used…
65relevance
17
PPubMed16 Dec 2022
Ensemble Model for Diagnostic Classification of Alzheimer’s Disease Based on Brain Anatomical Magnetic Resonance Imaging
Yusera Farooq Khan, Baijnath Kaushik, Chiranji Lal Chowdhary, Gautam Srivastava + 3 more
'Gautam Srivastava' 'Venkatesan Rajinikanth' 'Hong Lin' 'Snehalatha Umapathy'] Alzheimer’s is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain’s anatomy and enables the identification of diagnostic features. Artificial…
64relevance

Results for classification performance

A Systematic Comparison of Supervised Classifiers A Systematic Comparison of Supervised Classifiers

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network A Novel Hybrid Classification Model

Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis

Preference-Driven Classification Measure

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer

Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders

Modified Mahalanobis Taguchi System for Imbalance Data Classification

Performance analysis of data resampling on class imbalance and classification techniques on multi-omics data for cancer classification Performance analysis of resampling and classification techniques for cancer classification

Medical pattern classification using a novel binary similarity approach based on an associative classifier

SVM and SVM Ensembles in Breast Cancer Prediction SVM and SVM Ensembles in Breast Cancer Prediction

The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers

A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance

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

Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

Body composition predicts hypertension using machine learning methods: a cohort study

A comparative study in class imbalance mitigation when working with physiological signals

Ensemble Model for Diagnostic Classification of Alzheimer’s Disease Based on Brain Anatomical Magnetic Resonance Imaging

A Systematic Comparison of Supervised Classifiers A Systematic Comparison of Supervised Classifiers

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network A Novel Hybrid Classification Model

Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis

Preference-Driven Classification Measure

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer

Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders

Modified Mahalanobis Taguchi System for Imbalance Data Classification

Performance analysis of data resampling on class imbalance and classification techniques on multi-omics data for cancer classification Performance analysis of resampling and classification techniques for cancer classification

Medical pattern classification using a novel binary similarity approach based on an associative classifier

SVM and SVM Ensembles in Breast Cancer Prediction SVM and SVM Ensembles in Breast Cancer Prediction

The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers

A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance

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

Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

Body composition predicts hypertension using machine learning methods: a cohort study

A comparative study in class imbalance mitigation when working with physiological signals

Ensemble Model for Diagnostic Classification of Alzheimer’s Disease Based on Brain Anatomical Magnetic Resonance Imaging