The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 42-123
Full Name: Omar Saodah
Position: Lecturer
Age: ON
Sex: Female
Address: Faculty of Electrical Engineering
Country: MALAYSIA
Tel: +6043823363
Tel prefix:
Fax: +6043822819
E-mail address: saodah004@ppinnag.uitm.edu.my
Other E-mails: suai_narak@yahoo.com
Title of the Paper: An Automatic Voting Technique for the Innovated Multiple Multilayer Perceptron Network for Accuracy Classification Improvement
Authors as they appear in the Paper: Omar S, Salleh M.J, Saad Z, Osman M.K, Isa I.S
Email addresses of all the authors: saodah004@ppinang.uitm.edu.my, zuraidi570@ppinang.uitm.edu.my, khusairi@ppinang.uitm.edu.my,
Number of paper pages: 12
Abstract: An Artificial Neural Network (ANN) system has been extensively applied to numerous data classification problems such as cloud classification, business applications (sales forecasting), and medical domain for clinical diagnosis. The most well-known ANN architecture is the Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. However, the conventional ANN theory selects the best MLP (after training) for classification based on one which has the least number of hidden neurons, and gives the highest percentage of correct classification when if there are other MLPs (with more number of hidden neurons) which gives the same highest percentage of correct classification. The concept may not be correct since the other MLPs may perform better when presented with new datasets. Therefore, this project intends to investigate the capability of multiple MLP system with majority voting technique. It is a system which consists!
of all the best-performed MLPs and a single final output from these MLPs is selected by the voting system. The work employs neural network algorithm and C++ programming language as the tools to develop the proposed system. The MLP networks are trained using two types of learning algorithm, which are the Levenberg Marquardt and the Resilient Back Propagation algorithms. The performance of the multiple MLP networks are calculated based on the percentage of correct classificition. Data from two case studies; triangular waveform classification and breast cancer detection, have been used to test the performance of the developed system. The results show that the multiple MLP system with voting technique had the capability to improve the classification correctness. The advantages of this innovated method show that the MMLP system with automatic voting technique had the capability to improve the classification correctness.The most well-known artificial neural network (ANN) archite!
cture is the Multilayer Perceptron (MLP) network which is widely used
for solving problems related to data classifications. By approaching these invented MMLP system with automatic voting, the better classification result will be produce. With further advancement in the hardware of the system, "An automatic voting of the innovated (MMLP)" software has a vast potential to be prototyped as a commercial product.
Keywords: Automatic voting technique, multiple multilayer perceptron, artificial neural network, learning
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