Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5517
Title: A mixture of experts network structure for breast cancer diagnosis
Authors: Übeyli, Elif Derya
Keywords: Breast cancer diagnosis
Diagnostic accuracy
Expectation-maximization algorithm
Mixture of experts
Issue Date: 2005
Abstract: Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide diagnosing of breast cancer. Expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. Specifically, diagnosis is an attempt to accurately forecast the outcome of a specific situation, using as input information obtained from a concrete set of variables that potentially describe the situation. The ME network structure was implemented for breast cancer diagnosis using the attributes of each record in the Wisconsin breast cancer database. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. For the Wisconsin breast cancer diagnosis problem, the obtained total classification accuracy by the ME network structure was 98.85%. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. © 2005 Springer Science+Business Media, Inc.
URI: https://doi.org/10.1007/s10916-005-6112-6
https://hdl.handle.net/20.500.11851/5517
ISSN: 0148-5598
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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