On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Data fusion in robotics and machine intelligence
Data fusion in robotics and machine intelligence
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Protein secondary structure classifiers fusion using OWA
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
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Classifier fusion is a process that combines a set of outputs from multiple classifiers in order to achieve a more reliable and complete decision. In this work, the application of Ordered Weighted Averaging (OWA) operator as a classifier fusion approach, for diagnosing and offering the treatment of female urinary incontinence has been investigated. In this study, a classifier combination system has been constructed on four underlying individual classifiers, with different approaches including two multi-layer perceptrons, a generalized feed forward and a support vector machine. The system combines the decisions of these classifiers and is considered as a medical council based on only clinical patients data. Instead of choosing very accurate and expensive data sources like urodynamic, cystoscopy and voiding cystourethrogeram as paraclinical tests, we can nominate a small group of experts and use not so costly clinical measurements and then take experts' judgments and weight them by the level of expertise they have. Considering only clinical patient data which gathered from Iran urology medical center, the accuracy of OWA based classifier fusion system in diagnosis of urinary incontinence types improved 2.02%, 4.11% and 8.27% comparing the accuracy obtained by best individual underlying classifier, simple averaging and majority voting respectively.