Direct Rule Extraction and Classification with Incremental Neural Network

The purpose of this project is to present a direct method for rules extraction and he use of Neural Network for classification. A direct method with simplification (DMS) to extract rules from data using a hash table is also developed. Apart from the rules DMS, can also produce the corresponding decision tree for each subset with predefined class value. The merge of the paths of all the call values can produce the decision tree of the data. Therefore, DMS works as both direct and indirect method. The advantages of DMS are the ability to produce rules with a maximum number of conditions and prevention of subtree overlapping. Moreover an incremental Backpropagation Neural Network (IBNN) is created using the instances of data grouping according to their predefined class. Comparisons show that IBNN outperforms the DMS. Simulation results are provided.