Decision tree induction is used in expert systems for knowledge discovery. The main task performed in these systems is making use of inductive methods, given the values of attributes of an unknown object, to determine appropriate classification according to decision tree rules.
Input is supervised historical data. The system must learn from this data in order to generate a decision tree.
The decision tree shall classify instances by traversing from root node to leaf node. We start from root node of decision tree, testing the attribute specified by this node, and then moving down the tree branch according to the attribute value in the given set. This process is the repeated at the sub-tree level until a leaf node (class prediction) is reached.
The purpose of this project is to develop a web application that can be deployed on Glassfish and that implements a modified variation of decision tree learning algorithm ID3 (subject to constraints).
Financial institutions, Genomics supercomputer, Disease recognition, Telecommunications, Super markets.
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