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Exchange Risk Management and Measures

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Minimal ANN (MANN) model for Data Classification
Gunanidhi Pradhan, Bhubanananda Orissa School of Engineering, Cuttack gunanidhi_p@rediffmail.com Gadde Vyshnavi Kalyan,Final Yr IT,ANITS vyshv.sanjana@gmail.com Suresh Chandra Satapathy, MIEEE, Anil Neerukonda Institute of Technology & Sciences (ANITS), Vishakapatnam Dist sureshsatapathy@ieee.org Bhabatosh Mitra,FM University, Balasore bhaba_mit@yahoo.co.uk Sabyasachi Pattnaik,,FM University, Balasore spattnaik40@yahoo.co.in Abstract- Data Classification is a prime task in Data mining. Accurate and simple data classification task can help the clustering of large dataset appropriately. In this paper we have experimented and suggested a simple ANN based classification models called as Minimal ANN ( MANN) for different classification problems. The GA is used for optimally finding out the number of neurons in the single hidden layered model. Further, the model is trained with Back Propagation (BP) algorithm and GA (Genetic Algorithm) and classification accuracies are compared. It is revealed from the simulation that our suggested model can be a very good candidate for many applications as these are simple with good performances. Keywords- ANN, Genetic Algorithm, Data classification

I. INTRODUCTION Data classification is a classical problem extensively studied by statisticians and machine learning researchers. It is an important problem in variety of engineering and scientific disciplines such as biology, psychology, medicines, marketing, computer vision, and artificial intelligence [1]. The goal of the data classification is to classify objects into a number of categories or classes. Given a dataset, its classification may fall into two tasks i) supervised classification in which given data object is identified as a member of predefined class Unsupervised classification (or also known as Clustering) in which the data

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