Decision Trees for handling Uncertain Data to identify bank Frauds

Classification is a classical problem in machine learning and data mining. In traditional decision tree classification, a feature of a tuple is either categorical or numerical. The decision tree algorithms are used for classify the certain and numerical data for many applications. In existing system they implement the extended the model of decision tree classification to accommodate data tuple having numerical attributes with uncertainty described by arbitrary pdfs. So proposed work in this paper is new improved decision tree for both a data representing structure and a method used for data mining and machine learning. The decision trees assist in this work of selecting the attribute that will develop a better performance in finding the required uncertainty information to find the bank fraud. .