A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application

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dc.contributor.authorTalib, Hafizah
dc.date.accessioned2019-11-26T06:43:46Z
dc.date.available2019-11-26T06:43:46Z
dc.date.issued2014-02
dc.description.abstractMLP is a model of artificial neural network, which is simple yet successfully applied in various applications. The instability of MLP performance where small changes in training parameter could produce different models that inhibiting attainment of high accuracy in classification applications. In this research, an integrated system of Multi-Layer Perceptron Ensemble (MLPE) consisting of an MLPE and a new voting algorithm has been developed to increase classification accuracy and reduce the number of reject class cases. MLPE is produced from singular MLPs that are diverse in term of training algorithm and their initial weights. Three training algorithms used are Levenberg-Marquardt (LM), Resilient Backpropagation (RP) and Bayesian Regularization (BR). In order to choose the final output of MLPE, a new voting algorithm named Trust-Sum Voting (TSV) is proposed. The effectiveness of MLPE with TSV (MLPE-TSV) has been tested on four classification case studies which are Electrical Capacitance Tomography (ECT), Landsat Satellite Image (LSI), German Credit (GC) and Pima Indian Diabetes (PID). The performance of MLPE-TSV has been compared with the performance of MLPE which employs existing voting algorithms which are Majority Voting (MLPE-MV) and Trust Voting (MLPE-TV). The obtained results have shown that the proposed MLPE-TSV is capable of increasing the accuracy of classification as compared to singular MLPs, MLPE-MV and MLPE-TV. MLPE-TSV has also managed to reduce the number of cases in reject class.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/9216
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectVoting Techniqueen_US
dc.subjectMultilayer Perceptron Ensembleen_US
dc.titleA Voting Technique Of Multilayer Perceptron Ensemble For Classification Applicationen_US
dc.typeThesisen_US
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