Pawalai KraipeerapunSomkid AmornsamankulRamkhamhaeng UniversityMahidol UniversitySouth Carolina Commission on Higher Education2018-09-242018-09-242010-12-01International Conference on Applied Computer Science - Proceedings. (2010), 286-290179248632-s2.0-79958735976https://repository.li.mahidol.ac.th/handle/123456789/28987This paper proposes an approach to solve binary classification problems using Duo Output Neural Network (DONN). DONN is a neural network trained to predict a pair of complementary outputs which are the truth and falsity values. In this paper, outputs obtained from two DONNs are aggregated and used to predict the classification result. The first DONN is trained to predict a pair of truth and falsity values. The second DONN is trained to predict a pair of falsity and truth values. The target outputs used to train the second network are organized in reverse order of the first network. The proposed approach has been tested with three benchmarking UCI data sets, which are ionosphere, pima, and liver. It is found that the proposed techniques improve the performance as compared to feedforward backprogation neural network and complementary neural network.Mahidol UniversityComputer ScienceConference PaperSCOPUS