A forward only counter propagation network-based approach for contraceptive method choice classification task

This article proposes the forward only counter propagation network (FOCPN) for solving the contraceptive medical classification task. Contraceptive method choice (CMC) application is used for the medical classification and it is one of the challenging jobs in the field of the medicine. The experiments are performed on different radii of the neighbourhood and learning rate based on the size of the map. Experimental results show that FOCPN's convergence is faster and it gives the improved learning efficiency and reliable prediction performance. Also, the classification accuracy is much higher than the other models used for this purpose.

[1]  L. Buydens,et al.  Supervised Kohonen networks for classification problems , 2006 .

[2]  Tim Hendtlass,et al.  Using a modified counter-propagation algorithm to classify conjoined data , 2006, Applied Intelligence.

[3]  Young-Seuk Park,et al.  Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network. , 2003, Water research.

[4]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[5]  Marjan Vračko,et al.  Counter-propagation artificial neural network as a tool for the independent variable selection: Structure-mutagenicity study on aromatic amines , 2004, Molecular Diversity.

[6]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[7]  R. Hecht-Nielsen Counterpropagation networks. , 1987, Applied optics.

[8]  D. Jude Hemanth,et al.  Performance Improved PSO based Modified Counter Propagation Neural Network for Abnormal MR Brain Image Classification , 2010 .

[9]  Yuntao Qian,et al.  Semi-supervised Dynamic Counter Propagation Network , 2006, ADMA.

[10]  Zhongjie Wang,et al.  Application of Counter Propagation Network in Fault Diagnosis of Power Transformer , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[11]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[12]  Robert Hecht-Nielsen,et al.  Applications of counterpropagation networks , 1988, Neural Networks.

[13]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[14]  Chuan-Yu Chang,et al.  Copyright-proving scheme for audio with counter-propagation neural networks , 2010, Digit. Signal Process..

[15]  Liang Gao,et al.  Credit Scoring Model Based on Neural Network with Particle Swarm Optimization , 2006, ICNC.

[16]  Liang Gao,et al.  Combining Particle Swarm Optimization and Neural Network for Diagnosis of Unexplained Syncope , 2006, ICIC.