An Analysis of Large Data Classification using Ensemble Neural Network - Mumtazimah, Mohamad; Zahrahtul Amani, Zakaria; Wan Nor Shuhadah, Wan Nik; Arifah, Che Alhadi (2017)
In this paper, operational and complexity analysis are investigated for a proposed model of ensemble Artificial Neural Networks (ANN) multiple classifiers. The main idea to this is to employ more classifiers to obtain more accurate prediction as well as to enhance the classification capabilities in case of larger data. The classification result analysed between a single classifier and multiple classifiers followed by the estimates of upper bounds of converged functional error with the partitioning of the benchmark dataset. The estimates derived using Apriori method shows that proposed ensemble ANN algorithm with a different approach is feasible where such problem with a high number of inputs and classes can be solved with time complexity of O(n^k ) for some k, which is a type of polynomial. This result is in line with the significant performance achieved by diversity rule applied with the use of reordering technique. As a conclusion, an ensemble heterogeneous ANN classifier is practical and relevance to theoretical and experimental of combiners for ensemble ANN classifier systems for large dataset.