Search results for "K-fold"

showing 7 items of 7 documents

A kernel support vector machine based technique for Crohn’s disease classification in human patients

2017

In this paper a new technique for classification of patients affected by Crohn’s disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The eff…

Computer sciencebusiness.industryKernel support vector machineHuman patientK-fold cross-validation020206 networking & telecommunicationsPattern recognition02 engineering and technologyPredictive value030218 nuclear medicine & medical imagingSupport vector machine03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringClassification methodsCrohn disease classificationArtificial intelligencebusinessClassifier (UML)
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Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification

2019

Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-…

Crohn's diseasemedicine.diagnostic_testComputer sciencebusiness.industryFeature vectorFeature extractionSupervised learningMagnetic resonance imagingPattern recognitionmedicine.diseaseCrohn’s disease classification Feature extraction Feature reduction K-fold cross-validation Supervised learning Support vector machinesSupport vector machinemedicineMedical imagingArtificial intelligencebusinessReliability (statistics)
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A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.

2020

Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total …

Environmental Engineering010504 meteorology & atmospheric sciencesArtificial neural networkEnsemble forecastingElevationComputational intelligenceK-fold cross-validation (CV)Land cover010501 environmental sciences01 natural sciencesPollutionRandom forestSemnan PlainStatisticsDrawdown (hydrology)Land-subsidence susceptibilityEnvironmental ChemistryEnsemble methodWaste Management and DisposalGroundwaterEnvironmental Sciences0105 earth and related environmental sciencesMathematics
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A Novel System for Multi-level Crohn’s Disease Classification and Grading Based on a Multiclass Support Vector Machine

2020

Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient’s quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI…

Hyperparameterbusiness.industryComputer scienceMulticlass support vector machineBayesian optimizationSupervised learningFeature extractionFeature reductionCrohn’s disease multi-level classification and gradingK-fold cross-validationPattern recognitionSupport vector machineRadial basis function kernelMedical imagingFeature extractionArtificial intelligencebusinessClassifier (UML)Supervised learningBayesian optimization
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Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

2015

[EN] Non-parametric regression methods are powerful statistical methods to retrieve biophysical parameters from remote sensing measurements. However, their performance can be affected by what has been presented during the training phase. To ensure robust retrievals, various cross-validation sub-sampling methods are often used, which allow to evaluate the model with subsets of the field dataset. Here, two types of cross-validation techniques were analyzed in the development of non-parametric regression models: hold-out and k-fold. Selected non-parametric linear regression methods were least squares Linear Regression (LR) and Partial Least Squares Regression (PLSR), and nonlinear methods were…

TeledeteccióGeography Planning and Developmentlcsh:G1-922Least squaresCross-validationValidación cruzadaProcesos gausianosHold-outAnàlisi de regressióLinear regressionStatisticsPartial least squares regressionEarth and Planetary Sciences (miscellaneous)MLRAbusiness.industryCross-validationRegression analysisPattern recognitionRegresión de Kernel RidgeAprendizaje automáticoRegressionK-foldHold-OutGeographyk-foldPrincipal component regressionArtificial intelligencebusinessKernel Ridge regressionNonlinear regressionGaussian process regressionlcsh:Geography (General)Revista de Teledetección
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Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies

2018

Positron Emission Tomography (PET) imaging is increasingly used in radiotherapy environment as well as for staging and assessing treatment response. The ability to classify PET tissues, as normal versus abnormal tissues, is crucial for medical analysis and interpretation. For this reason, a system for classifying PET area is implemented and validated. The proposed classification is carried out using k-nearest neighbor (KNN) method with the stratified K-Fold Cross-Validation strategy to enhance the classifier reliability. A dataset of eighty oncological patients are collected for system training and validation. For every patient, lesion (abnormal tissue) and background (normal tissue around …

Treatment responsepositron emission tomographyK-nearest neighborKernel support vector machineComputer scienceNormal tissueK-Fold cross-validation030218 nuclear medicine & medical imagingk-nearest neighbors algorithmLesion03 medical and health sciences0302 clinical medicinetissue classificationmedicineRadiation treatment planningFuzzy C-Mean1707Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryPattern recognitionComputer Graphics and Computer-Aided DesignPredictive valueSupport vector machineFuzzy C-MeansPositron emission tomography030220 oncology & carcinogenesisComputer Vision and Pattern RecognitionArtificial intelligencemedicine.symptombusinessPattern Recognition and Image Analysis
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Data from: Fine-scale population dynamics in a marine fish species inferred from dynamic state-space models

2018

Identifying the spatial scale of population structuring is critical for the conservation of natural populations and for drawing accurate ecological inferences. However, population studies often use spatially aggregated data to draw inferences about population trends and drivers, potentially masking ecologically relevant population sub-structure and dynamics. The goals of this study were to investigate how population dynamics models with and without spatial structure affect inferences on population trends and the identification of intrinsic drivers of population dynamics (e.g. density dependence). Specifically, we developed dynamic, age-structured, state-space models to test different hypoth…

medicine and health careage-structured modelSpatial structuretime-seriesk-fold cross validationLife SciencesMedicinestate-space modelsBayesian
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