Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer's Dementia

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Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer's Dementia is …
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scholarly articleQ13442814

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P8978DBLP publication IDjournals/cmmm/SivapriyaKT15
P356DOI10.1155/2015/676129
P932PMC publication ID4632180
P698PubMed publication ID26576199
P5875ResearchGate publication ID283958416

P2093author name stringA R Nadira Banu Kamal
P Ranjit Jeba Thangaiah
T R Sivapriya
P2860cites workRandom forest-based similarity measures for multi-modal classification of Alzheimer's diseaseQ36451170
Comparing Diagnostic Accuracy of Cognitive Screening Instruments: A Weighted Comparison ApproachQ36744317
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An ensemble feature selection technique for cancer recognitionQ44074355
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Prediction of All-Cause Dementia Using Neuropsychological Tests within 10 and 5 Years of Diagnosis in a Community-Based SampleQ51034696
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Support-vector networksQ55922708
Gene Selection for Cancer Classification using Support Vector MachinesQ56535529
A survey of cross-validation procedures for model selectionQ56689497
Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival ForestsQ22673963
Use of data mining techniques to determine and predict length of stay of cardiac patientsQ28676973
Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized methodQ28756638
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Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systemsQ30682314
Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetesQ33544007
Detection of early Alzheimer's disease in MCI patients by the combination of MMSE and an episodic memory testQ33942367
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 foQ33995156
Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI databaseQ34120412
The diagnosis of young-onset dementia.Q34165340
Predicting conversion from mild cognitive impairment to Alzheimer's disease using neuropsychological tests and multivariate methodsQ34625484
An AUC-based permutation variable importance measure for random forestsQ34656816
Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI.Q34850136
Missing Data Methods for Partial CorrelationsQ34990720
Automated VOI Analysis in FDDNP PET Using Structural Warping: Validation through Classification of Alzheimer's Disease PatientsQ35846002
The use of classification trees for bioinformaticsQ35896436
P275copyright licenseCreative Commons Attribution 3.0 UnportedQ14947546
P6216copyright statuscopyrightedQ50423863
P921main subjectfeature selectionQ446488
Alzheimer's diseaseQ11081
P304page(s)676129
P577publication date2015-10-20
P1433published inComputational and Mathematical Methods in MedicineQ15754930
P1476titleEnsemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer's Dementia
P478volume2015

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cites work (P2860)
Q57047326Earlier detection of Alzheimer disease using N-fold cross validation approach
Q94672454Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer's Disease
Q89173711How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database
Q45944732Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review.

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