A hybrid expert system approach for telemonitoring of vocal fold pathology
Applied Soft Computing
Advances in Fuzzy Systems - Special issue on Fuzzy Methods and Approximate Reasoning in Geographical Information Systems
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hotspots detection in spatial analysis via the extended gustafson-kessel algorithm
Advances in Fuzzy Systems - Special issue on Fuzzy Methods and Approximate Reasoning in Geographical Information Systems
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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.