Classification accuracy
Improving ECG classification accuracy using an ensemble of neural network modules.
... in classification particularly for difficult problems such as those involving a considerable amount of noise, limited number of patterns, high dimensional feature sets, and highly overlapped ...
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100% classification accuracy considered harmful: the normalized information transfer factor explains the accuracy paradox.
... performance, accuracy, suffers from a paradox: predictive models with a given level of accuracy may have greater predictive power than models with higher ...optimizing classification error rate, high ...
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USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION
... is classification. The problem of traditional classification methods is that each pixel is assigned to a single class by presuming all pixels within the ...pixel classification or spectral unmixing, ...
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Data mining methods in the prediction of dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests
... for classification trees (median AUC of ...total accuracy of 8 of the 10 evaluated classifiers (Medians between ...total accuracy, SVM and RF rank highest amongst the classifiers tested as has been ...
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MAPPING AND CHANGE ANALYSIS IN MANGROVE FOREST BY USING LANDSAT IMAGERY
... context, accuracy regarded to the degree of correctness of a classified map, and it is comprised of bias and ...with classification may be considered as accurate if it provided an unbiased representation of ...
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Using generic summarization to improve music information retrieval tasks
... Generic summarization algorithms define and detect rele- vance and diversity of the input signal, satisfying our need for a more informed way of selecting the most important parts to fit in 30-second summaries. The ...
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Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood
... for classification of the surface ...and classification of elevated features such as buildings and ...improving classification accuracy in urban ...based classification method is ...
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Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China.
... the classification output of machine learning ...crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties ...
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USO DE IMAGENS MULTIESPECTRAIS ASTER PARA MAPEAR ESPÉCIES INVASORAS LENHOSAS NA RESERVA NATURAL DE PICO DA VARA (AÇORES, PORTUGAL)
... user accuracy = 0.96 and 9B’s user accuracy = ...the classification accuracy of Pittosporum woodland: (1) it can be significantly improved by including, in the classification scheme, ...
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EFFECTIVENESS PREDICTION OF MEMORY BASED CLASSIFIERS FOR THE CLASSIFICATION OF MULTIVARIATE DATA SET
... of Classification Accuracy, RMSE and MAE values as shown in Table ...the classification accuracy, MAE and RMSE values and it is given in Table ...100% accuracy and 0 MAE and RMSE got ...
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Classifiers ensemble in remote sensing: a
... Trees: Classification tree analysis provides an effective collection of algorithms for classifying remotely sensed data, but has the limitations of not searching for the optimal tree structure or being adversely ...
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A kernel-based multivariate feature selection method for microarray data classification.
... Motivated by mentioned above, in this paper, we develop a feature selection method based on the partial least squares(abbre- viated PLS) [21] and theory of Reproducing Kernel Hilbert Space [22], we called it ...
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Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.
... same classification feature across ...lowest classification accuracy using delta band power over the left motor regions achieved much higher prediction accuracy using beta band power over ...
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Cross-platform normalization of microarray and RNA-seq data for machine learning applications
... better preserves some of the signal at high noise levels because it changes the data the least, while nonparanormal may do so by separating the marginal distributions of each gene. Overall, nonparanormal transformation ...
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Int. braz j urol. vol.30 número1
... Results: Agreement between MRI and surgical-pathologic staging was good for T staging (kappa = 0.72 and 0.78 for reviewers 1 and 2 respectively), poor for N staging (kappa = 0.13, both reviewers), good for M staging ...
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Diagnosis of Alzheimer's disease from 3D images of the brain along time
... Alzheimer’s disease (AD) is the most common cause of dementia in the elderly. Although no cure has yet been found for this disorder, it is possible to delay progression of its symptoms if therapeutic intervention is ...
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IMPROVEMENT OF THERMAL ESTIMATION AT LAND COVER BOUNDARY BY USING QUANTILE
... cover classification was conducted for Landsat ETM image of ...likelihood classification algorism was used for this purpose. Classification classes were urban, water body, forest, soil, bare ground1, ...
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Brain Imaging Analysis Can Identify Participants under Regular Mental Training
... The current study evaluated whether the information contained in structural (T1 weighted) images was capable of predicting or discriminating between regular meditators and non-meditators. The volumes of each segmented ...
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Analyzing gene expression from whole tissue vs. different cell types reveals the central role of neurons in predicting severity of Alzheimer's disease.
... built classification models utilizing different available datasets containing gene expression from two different regions: the cortex and the ...section. Classification models for the disease stages ...
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GEOSTATISTICS AND REMOTE SENSING METHODS IN THE CLASSIFICATION OF IMAGES OF AREAS CULTIVATED WITH CITRUS ALESSANDRA F.SILVA
... It was found that the result in the evaluation of quality of the classification was strong for bands 2 and 4 of IK classifier and for band 2 of Cluster classifier, with an index of 0.6546, 0.6226 and 0.7548, ...
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