2008
DOI: 10.1016/j.eswa.2007.08.067
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Multiclass support vector machines for diagnosis of erythemato-squamous diseases

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Cited by 75 publications
(31 citation statements)
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“…The performance of MLPNN was enhanced using the scored predictions of C5.0 DT and LDA models in the proposed ensemble arrangement. Classification accuracies of the ensemble were very close to those achieved by the work of E. Ubeyli [16] using multiclass support vector machine model. Chul-Heui Lee et al [17] proposed a new classification method based on the hierarchical granulation structure using the rough set theory.…”
Section: Classification Techniques On Healthcare Datasupporting
confidence: 65%
“…The performance of MLPNN was enhanced using the scored predictions of C5.0 DT and LDA models in the proposed ensemble arrangement. Classification accuracies of the ensemble were very close to those achieved by the work of E. Ubeyli [16] using multiclass support vector machine model. Chul-Heui Lee et al [17] proposed a new classification method based on the hierarchical granulation structure using the rough set theory.…”
Section: Classification Techniques On Healthcare Datasupporting
confidence: 65%
“…4 displays the ROC curve to show the performance of LS-SVM for EEG recording data set of two classes. A good test is the one for which sensitivity (true positive rate) rises rapidly and 1-specificity (false positive rate) hardly increases at all until sensitivity becomes high [11]. ROC curve in Fig.…”
Section: Performance Evaluation Measurementsmentioning
confidence: 99%
“…Luukka presented similarity classifier using similarity measure derived from Yu's norm for classifying medical data sets [10], and the classification accuracy for the diagnosis of erythemato-squamous diseases was 97.8%. Ubeyli obtained 98.32% classification accuracy on the differential diagnosis of erythemato-squamous diseases [11], using multiclass support vector machines with the error correcting output codes. Polat and Gunes obtained 96.71% classification correct rate on diagnosis of erythemato-squamous diseases using a novel hybrid intelligence method based on C4.5 decision tree classifier and one against all approach for multi-class classification problem [12].…”
Section: Related Workmentioning
confidence: 99%