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Improved Classification Performance for Multiple Multilayer Perceptron (MMLP) Network Using Voting Technique

This project investigates the capability of multiple multilayer perceptron (MMLP) 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 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 MMLP networks are calculated based on the percentage of correct classificition. Data from three case studies, triangular waveform classification, breast cancer detection and transport classification, have been used to test the performance of the developed system. The results show that the MMLP system with voting technique has the capability of improving the classification correctness. The most well-known artificial neural network (ANN) architecture is a Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. By approaching these innovated MMLP system with automatic voting, the better classification result will be produced.