Modular Neural Network Approach for Data Classification

Classification is a challenging task that has important application in real life and its application are excepted to grow more in future. In this paper, we analyze the effectiveness of Modular Neural Network as a modelling tool for data classification. The MNN classifier outperforms the surveyed nets due to its novel task decomposition and multi-module decision-making techniques. In this paper, we present a MNN architecture for supervised learning. The basic building block of the architecture are multilayer feed forward neural network with back propagation algorithm. MNN is consider as one of the state-of-art system as feature extractors and classifier and are proven to be very efficient in analyzing problem with complex feature space. The aim of this work is achieve by five bench mark problemMagic Gamma Telescope Data set, Liver Disorder Data set, Balance Scale Data set, Monk’s Problem Data set, Yeast Data Set. Experiment describe in this paper show that the architecture is especially useful in solving problems with a large number of input attributes. Keywords– Classification, Modular neural network, feedforward neural network, back-propagation algorithm.