J Med Syst - Application of attribute weighting method based on clustering centers to discrimination of linearly non-separable medical datasets.

Tópicos

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Resumo

In this paper, attribute weighting method based on the cluster centers with aim of increasing the discrimination between classes has been proposed and applied to nonlinear separable datasets including two medical datasets (mammographic mass dataset and bupa liver disorders dataset) and 2-D spiral dataset. The goals of this method are to gather the data points near to cluster center all together to transform from nonlinear separable datasets to linear separable dataset. As clustering algorithm, k-means clustering, fuzzy c-means clustering, and subtractive clustering have been used. The proposed attribute weighting methods are k-means clustering based attribute weighting (KMCBAW), fuzzy c-means clustering based attribute weighting (FCMCBAW), and subtractive clustering based attribute weighting (SCBAW) and used prior to classifier algorithms including C4.5 decision tree and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed method, the recall, precision value, true negative rate (TNR), G-mean1, G-mean2, f-measure, and classification accuracy have been used. The results have shown that the best attribute weighting method was the subtractive clustering based attribute weighting with respect to classification performance in the classification of three used datasets.

Resumo Limpo

paper attribut weight method base cluster center aim increas discrimin class propos appli nonlinear separ dataset includ two medic dataset mammograph mass dataset bupa liver disord dataset d spiral dataset goal method gather data point near cluster center togeth transform nonlinear separ dataset linear separ dataset cluster algorithm kmean cluster fuzzi cmean cluster subtract cluster use propos attribut weight method kmean cluster base attribut weight kmcbaw fuzzi cmean cluster base attribut weight fcmcbaw subtract cluster base attribut weight scbaw use prior classifi algorithm includ c decis tree adapt neurofuzzi infer system anfi evalu propos method recal precis valu true negat rate tnr gmean gmean fmeasur classif accuraci use result shown best attribut weight method subtract cluster base attribut weight respect classif perform classif three use dataset

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