An effective and novel wavelet neural network approach in classifying type 2 diabetics

Title:

An effective and novel wavelet neural network approach in classifying type 2 diabetics

Creator:

Zainuddin, Zarita and Ong, Pauline

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:21b2c5e7-5c4a-4a6b-a3da-f4719aca4203
uuid:21b2c5e7-5c4a-4a6b-a3da-f4719aca4203
doi:10.14311/NNW.2012.22.025

Subject:

Clustering, diabetes, fuzzy-cmeans, microarray, and wavelet neural network

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm - specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm - in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:5

Harvested from:

CDK

Metadata only:

false